Study of the radiation effects on the Swift-XRT CCD camera in low Earth orbit

Transcription

1 Study of the radiation effects on the Swift-XRT CCD camera in low Earth orbit Thesis submitted for the degree of Doctor of Philosophy at the University of Leicester by Claudio Pagani Department of Physics and Astronomy University of Leicester 2015

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3 Study of the radiation effects on the Swift-XRT CCD camera in low Earth orbit Claudio Pagani Abstract In this thesis, the damage caused by space radiation on the Swift X-ray telescope CCD is investigated. The analysis reveals the presence of damaged pixels affected by charge traps that results in the degradation of the detector energy resolution. The software developed for the trap mapping analysis is presented. The implementation of the trap corrections recovers a significant fraction of the lost resolution. Data from XRT calibration radioactive sources are analysed to characterise the energy and temperature dependence of the charge losses. The trap measurements are exploited in the attempt to derive the value of the ionisation energy of silicon using a novel statistical method. The large charge losses affecting the damaged pixels are at odds with expectations from CCD irradiation by protons, that should generate single electron defects. Neutrons, instead, generated on board the XRT in the detector aluminium proton shield, may displace multiple lattice atoms, as they interact directly with the nuclei. The two scenarios were investigated exposing the same kind of CCD on board the XRT, irradiated before the Swift launch with 10 MeV protons, to a dose of 14 MeV neutrons comparable to that of the XRT during a few years in orbit, as derived from simulations developed using ESA s space radiation modelling system. A laboratory program was undertaken at the Leicester Camera test facility to investigate the damage caused by protons and neutrons. In both cases, pixels affected by large energy losses are identified and characteristic trap energy levels are derived. In the context of satellite missions using CCDs, the observed spatial nonuniformity of the damage suggests that the classical approach of an average correction for the charge transfer inefficiency applied over the entire detector i

4 ii is not accurate and may produce misleading results. Optimisation of the CCD shielding design is discussed based on this investigation.

5 Declaration I declare that no part of this thesis has been previously submitted to this or any other university as part of the requirements for a higher degree. The work described herein was conducted solely by the undersigned except for those colleagues and other workers acknowledged in the text. Some of the results reported in this thesis have been included in the following publication. Recovering Swift-XRT energy resolution through CCD charge trap mapping, Pagani, C. et al., Astronomy & Astrophysics, Volume 534, A20, 2011 Claudio Pagani January 2015

6 iv Dedication To Debora, Samuele and Olivia Acknowledgements My PhD yearshave been agreat opportunitytomeet andgetto knowmany nice people that encouraged me and kindly gave some (or a lot) of their time to help with my work; so, here I wish to thank: My supervisor, Prof. Julian Osborne, for the opportunity he gave me to work on this thesis while being part of the Swift team, for his suggestions and comments (very useful after decryption) and for always finding the time to meet and discuss my work. Thanks to my colleagues and office mates, Andy Beardmore for the many discussions about radiation damage and traps and detector response over lunch break, Chris Mountford for the processing of the corner sources, Kim Page and Phil Evans for their support and mad conversations in the office. Thanks to my co-advisor, Prof. Paul O Brien, for his suggestions, and thanks to my tutor, Prof. George Fraser, and to Tony Abbey; with their vast knowledge of CCDs, they always showed great interest in my work and were precious sources of brilliant ideas and comments. I m also grateful to George for the discussions on the measurement on the ionisation parameter, and to Simon Vaughan for his suggestions on statistics. Thanks to Steve Sembay for his help with the XMM data analysis. Thanks to David Vernon, the Leicester Camera Test Facility head engineer, forhissupportwiththelaboratoryprogramandwiththeexpeditiontorometo irradiate the CCD at the Frascati Neutron Generator facility, somehow managing to go through airport security with all the CCD equipment without raising suspicion. The use of the Frascati Generator was made possible by Mario Pillon, the head engineer of the facility, that alongside his colleagues provided essential support during the irradiation experiment, grazie! And many, many thanks to my wife Debora for always being there with her love and support and to my great kids, Samuele and Olivia, for always making me smile.

7 Contents 1 Introduction Overview Charge Coupled Devices Electron-hole generation Charge collection and transfer Noise The Swift X-ray telescope XRT data format and processing Radiation damage Space radiation environment Ionising radiation and displacement damage Charge Transfer Efficiency XRT radiation damage Thesis Outline Radiation damage of the Swift-XRT CCD and charge trap mapping Introduction Trap mapping Photon Counting mode Windowed Timing mode Energy dependence Temperature dependence Recovered energy resolution Trap mapping limitations Trap mapping applications v

8 vi 2.8 Summary The XRT calibration corner sources and a novel method to derive the electron-hole pair creation energy of silicon Introduction Data processing Trap detection and history Results Temperature dependence Recovery effect and trap energy levels Location and size of the damage cluster A new method to derive the mean electron-hole pair creation energy in silicon Modified χ 2 minimisation technique Numerical simulations Results Analysis of the XMM calibration data to derive the ionisation energy of silicon Summary Characterisation of charge traps in a proton-damaged CCD Introduction CCD irradiation Instrumentation Data processing and analysis Pre-irradiation dataset Temperature dependence Data samples and results Energy dependence Sacrificial charge analysis Sacrificial charge in datasets with standard flux Trap emission time constant Manufacturing induced traps Summary

9 vii 5 Neutron irradiation and damage Introduction Exposure of the XRT CCD to secondary neutrons Neutron flux estimate Rate of interaction of secondary neutrons with silicon Neutron irradiation Neutron damage analysis Temperature dependence Energy dependence Sacrificial charge analysis Recovery effect Summary Conclusions Summary Future prospects

10 List of Tables 1.1 Physical properties of common traps generated by irradiation, from Philbrick (2002) and Seabroke et al. (2008). The emission time constant is tabulated at T= 100 C XRT instrumental full width half maximum (FWHM) in the observed and the corrected spectra of each calibration epoch (specified as YYYY/MM). The total exposure time of the observations used for the trap analysis for each epoch is reported in kiloseconds (ks). The FWHM values at sulphur and iron are only reported when enough counts in the lines allowed a reliable fit. The last column quantifies the improvement in the energy resolution R defined as R = (R O R C ) R O, where R = FWHM E 3.1 Mean energy levels of the traps in the damaged pixels, as derived from the best fit of the energy profile recoveries, for a sample of representative values of σx Simulations to derive the confidence level in the estimate of the ionisation energy in silicon by minimising the modified χ 2 function. The trials were based on the distribution of the preliminary trap depth measurements and their uncertainties, fixing ω to 3.7 ev. An error coefficient between 1.0 and 0.3 was tested to scale the preliminary trap depth uncertainties. The percentage of trials when ω was recovered using the χ 2 minimisation technique within the ranges [3.6,3.8] and [3.4,4.0] is reported here as a function of the trap depth error level viii

11 ix 4.1 Traps depths (in ev) at fixed CCD operational temperatures between 100 C and 50 C from titanium Kα (4.510 kev) fluorescence line energy datasets. When no significant trap depth was measured a blank entry was left in the table. Manufacturing defects are reported at the end of the table, starting with damaged pixel (132,104) Traps depths (in ev) measured at the oxygen Kα (524.9 ev), copper Lα (929.7 ev), silicon Kα (1.740 kev), titanium Kα (4.510 kev) and iron Kα(6.404 kev) fluorescence line energies at 75 C. A blank entry is entered when the trap depth could not be confidently measured. Manufacturing defects are reported at the end of the table, starting with damaged pixel (132,104) Trap depth energy dependence. Power law indices α from the best fit of the energy dependence of the charge losses using a power law function Capture cross section σ c for a sub-sample of pixels with a steepto-flat energy dependence of the trap depth. The cross section values were derived by fitting the measured charge losses as a function of the charge density of the X-ray events Energy levels of the traps in the damaged pixels, as derived from the best fit of the energy profile recoveries, for a sample of representative values of σx Energy losses (in ev) in pixels damaged by neutron irradiation, measured at CCD temperatures between 100 C and 50 C from titanium Kα (4.510 kev) fluorescence line energy datasets. When no significant trap depth was measured a blank entry was left in the table Energy levels of the traps in the neutron damaged pixels, as derivedfromthebestfitoftheenergyprofilerecoveriesatt= 60 C, for a sample of representative values of σx Number of single-electron traps and cluster defects measured after proton and neutron irradiation and corresponding expected damage constant for each type of particle and defect

12 List of Figures 1.1 The metal oxide semiconductor (MOS) is the basic unit of a CCD. The gate electrode is deposited on top of a doped silicon substrate with an oxidised surface (SiO 2 ) Buried channel potential well. The introduction of a thin n- doped region shifts the minimum of the potential in the silicon away from the interface with the oxidised surface, where trapping sites can capture signal charges. Figure from Janesick (2001) Spectral redistribution function, showing the primary photopeaks, the escape peaks and the fluorescence peaks, from an input spectrum generated by the XRT 55 Fe door source shortly after launch, producing Mn-Kα (5890 ev) and Mn-Kβ (6490 ev) lines Charge transfer in a pixel in a three phase CCD; the electrons move under the electrodes kept at high voltage Diagram of the X-ray Telescope (Burrows et al., 2005) Exploded view of the XRT; figure from the PSU XRT website Schematic view of the CCD-22 on board the XRT. In the image region, the field of view is defined by the structure of the optical filterhousing; four 55 Fecalibrationsourcesilluminatethecorners of the image section. Images are transferred from the image to the covered store section, and readout through the serial register to the output amplifier XRT CCD-22 SEM images. (Left panel) - Top view of the pixel, showing the open electrode surface (OE) and the electrode finger (F) (Right panel) - Side view of the pixel, showing the etched electrode layer, the nitride, oxide and epitaxial silicon layer x

13 xi 1.9 XRT operational modes. Images in detector coordinates of two point sources, observed in Photon Counting mode(pc, left panel), and Windowed Timing mode (WT, right panel). PC mode is used for fainter sources, and provides 2D spatial information; WT mode has better timing resolution at the expense of spatial information, and is used for brighter targets SRIM simulation of the number of displacements caused in a CCD by incoming protons perpendicular to the detector surface of energies between 250 kev and 1 MeV, from Janesick (2001) Temperature dependence of the emission time of some of the most common types of radiation induced traps, given with their trap energy E t Swift-XRT CCD gain and CTI measurements from the corner source data at a CCD temperature of -60 C from 2007-Sep-05 to 2012-Sept-30. The top-left panel shows the measured 55 Fe K-α line energy fitted with a Gaussian function and the bottomleft panel shows the measured CTI values. The measurements are used to derive the gain and CTI coefficients (top right and bottom right panels) used in the XRT calibration gain file. Error bars are 1σ estimates. In the plot, the times are in MET, Mission Elapsed Time, the Swift spacecraft time, that uses as reference epoch the 1st of January, The Figure is from A. Beardmore, XRT calibration scientist XRT WT mode Cas A spectrum in 2005 and in 2010 (Pagani et al., 2011). The comparison shows an overall energy shift resulting from charge loss and the reduced energy resolution that causes the broadening of the brighter lines and the complete disappearance of the weaker ones. The silicon Kα line E = kev has a FWHM of 101± 3 ev and of 220± 12eV in the 2005 and 2010 datasets respectively, as measured in IDL using a modified Gaussian function (f e (x E) 2 2σ1 2 for x E, f e (x E) 2σ2 2 2 for x < E) to model the asymmetric distortion of the spectral lines caused by trap losses and a linear component to model the local continuum

14 xii 2.1 Deep charge traps were found in pre-launch test data(from Morris (2005)). On the left panels, the energy of the events from the Mn Kα line (in DN units) are plotted along the columns; on the right panels, the median energy value of the Mn Kα events along the affected columns is shown. Deep traps are visible in columns 54, 78 and 110, while shallower energy losses are measured in pixels of columns 140, 259 and Fe door source datasets collected shortly after Swift launch. Top panels - Traps with large trap losses in columns 66 and 324; Bottom panels - Columns 281 and 351 present overall energy offsets caused by traps in the CCD frame store section Trap mapping observations. (Left panel) - February 2009 Cas A campaign - Mosaic of the Cas A offset pointings displayed in detector coordinates (DETX are the columns and DETY the rows coordinates). The remnant covers approximately an area of 100 pixels radius of the detector in a single pointing. (Right panel) - Tycho trap mapping observations in detector coordinates; the set of offset pointings are aimed at obtaining the largest statistics in the central region of the CCD, where the majority of science targets are imaged in pointed observations. The images are scaled differently for Cas A and Tycho observations to evidence the location of the various offset pointings ThegaincoefficientatT= 60 C, estimatedfromthefivecolumns with the highest Mn Kα line energy in CS3, is used to process trap-corrected spectra (in green the measured monthly averaged gain values, in red the modelled gain evolution with time). Error bars are 1σ estimates Profile of the Si-Kα line energy in column 256 from Tycho trap mapping observations. These plots are used to identify columns affected by deep traps, as the one present in row DETY= The Si-Kα line energy in column 256 after trap corrections are applied processing the Tycho calibration observations using the updated gain file. The energy of the line is restored to the expected value after trap corrections have been applied

15 xiii 2.7 Charge losses measured from PC and WT mode observations of thetycho SNRinAugust of2013, using thesi-kα linereference energy E ref = kev. A linear correlation with index α = 0.95±0.06andinterceptβ = 141±8welldescribesthedatabuta large scatter in the datapoints prevents the use of energy losses measured in PC mode to estimate and correct for the energy offsets in WT observations Energy dependence of the charge losses. (Left panel)- the instrumental nickel Kα line is used to derive the energy dependence of the charge losses above E break ; the observed line resolution and flux (in black) improves after trap corrections are applied (in red). (Right panel) - SNR E is used to derive the energy dependence below E break ; the comparison of the observed spectrum (in black) and the trap-corrected spectrum (in red) evidences the improved spectral resolution after the appropriate energy dependence has been derived The temperature dependence of the trap losses causes variations in the measured energy of the Si-Kα in Tycho PC observations and in the Ni-Kα background instrumental line (top panels). The implementation of the observed dependence in the PC gain file brings the energy of the lines close to the expected value at all temperatures (bottom panels, data from Tycho August 2013 and nickel 2013 PC datasets) The effects of radiation damage as seen in Cas A observations taken in early 2005 and in late The top panels compare the spectra extracted from PC data, in the 1-8 kev energy range (left panel) and around the Fe-Kα emission line (right panel). The bottom panels compare spectra from WT data focusing on the energy range of the stronger emission lines (left panel) and the Fe-Kα emission line (right panel)

16 xiv 2.11 Recovery of spectral energy resolution. (Left panel) - Comparison of the observed and the trap corrected spectra extracted from WT observations of the Cas A SNR in October The fit of the Si-Kα line with an asymmetric Gaussian in IDL and a linear component to model the local continuum yielded FWHM =159±13 evfortheobserved2007spectrumand106±3 evfor the corrected 2007 spectrum. For comparison, the FWHM value during an observation in February 2005 (shortly after launch) was 101 ± 3 ev; (Right panel) - Results of trap mapping and corrections from PC mode observations of the Tycho SNR taken in October As radiation continues to damage the CCD the spectral resolution worsens, with a FWHM of the Si line of 132± 3 ev after trap corrections in this case Loss in spectral resolution and recovered fraction thanks to trap energy corrections between Cas A observations taken in early 2005 and in late (Left panel) - PC mode observations; (Right panel) - WT mode observations Evolution of the FWHM of the Si-Kα line of the Cas A (in 2007 and 2008) and of the Tycho (since 2009) SNRs, in observed and trap-corrected spectra for PC and WT mode data. At the beginning of the mission, in February 2005, the Si-Kα line of Cas A spectra had a measured FWHM PC = 108± 4 and FWHM WT = 101± Trap correction application. The 5.5 ks observation of the Cas A SNR taken in August 2010 in PC mode demonstrates the validity of trap corrections when applied on datasets other than those used to define the trap mapping calibrations. The Si-Kα line has FWHM of 167± 10 ev in the observed spectrum and of 135± 7 ev after trap corrections derived from Tycho calibration observations from March 2010 are applied

17 xv 2.15 Swift triggered on a flare of the EV Lac star in April of 2008, promptly slewing to observe the source with the narrow field instruments. The XRT lightcurve (left panel) consists of WT data during the first orbit of observations, followed by PC data in subsequent orbits as the source had faded. A fluorescent iron line at 6.4 kev was reported in the analysis by Osten et al. (2010), who processed the data with the XRT software and gain files available at that time, that did not include trap corrections (right panel) The effect of charge traps in the spectrum of EV Lac. (Left panel) -The April 2008 WT observed spectrum (in black) of the flarestar EVLacafterthesource hasfadedtoacount ratebelow 150 cts s 1 is compared to the trap-corrected spectrum (in red). The corrections enhance the main spectral peak at 6.7 kev, but no improvement is seen in the definition of the proposed emission feature at 6.4 kev. (Right panel) - Using the previous, non trapcorrected gain file, the WT observed spectrum of EV Lac during the late decay of the flare (in black) is compared to the spectrum extracted from the columns most affected by radiation damage (in red). Charge traps cause a shift of the X-ray events to lower energies, that in the most damaged columns results in a bump at energies between 6.3 kev and 6.6 kev Time evolution of the measured intensity of the radioactive corner sources (half-life of years) Total number of pixels with charge traps causing energy losses equal or greater than approximately 20 ev in the June December 2013 time period. The statistics begins in June 2008, when the PC mode window was widened and the full corner source area started to be telemetered to the ground. A faster increase in trap number is seen during Energy losses for a sample of damaged corner source pixels

18 xvi 3.4 Distribution of trap energy losses measured over 6-month long integration epochs since the trap onsets, using corner source single pixel events only and selecting CCD temperatures between 65 C and 55 C Temperature dependence of the energy losses in corner source trapped pixels. (Top panels) - For the majority of damaged pixels the losses decrease at the warmer CCD temperatures, as the thermally generated noise partially fills the traps in the damage cluster. In some cases (top right panel) a flattening is seen at the coldest temperature as some of the captured charge is frozen. (Middle panels) - The captured charge of these pixels starts to freeze at T 60 C, resulting in a reduction of the charge losses at colder temperatures. (Bottom panels) - In a small number of cases the energy losses are constant across the investigated temperature range Recovery effect. Damaged pixels for which the measured X-ray energy appears to be gradually and partially restored the farther above the trap the photon is detected. From top to bottom, the cases in columns 62, 68, 79, 595, 26 and 25 are shown here, with the measured event energy plotted as a function of the row detector coordinate (DETY). Data are extracted selecting three distinct CCD temperature intervals centred at T= 56 C, 60 C and 64 C. The recovery is generally faster at higher temperatures (top panels), while in other cases the recovery appears more gradual and similar at the three investigated temperatures (middle panels). The recovery is also seen for damage extended over two pixels (bottom left panel) and in a case where the damage is partially located in the volume of the pixel where the charge from the detected X-rays is generated and collected (bottom right panel) The corner source analysis revealed the presence of several isolated pixels with a lower measured Mn-Kα line energy with respect to the neighbouring pixels, as in the case of the damaged pixel (74,548), left panel, and (545,544), right panel

19 xvii 3.8 (Left panel) - The χ 2 minimisation function of Equation 3.8, calculatedforaset of100trapsofenergylosses randomlygenerated from a uniform distribution of values in the range ev, is larger for increasing values of ω. (Right panel) - The same function divided by ωguess Example of a distribution of trapped electrons (top panel) and errors (bottom panel) generated in IDL and used in the simulations to test the validity of the χ 2 technique. These distributions were generated using the genrand routine, that generates random numbers from an arbitrary input distribution by inverting the cumulative probability function. The input distribution consisted of the preliminary trap depth measurements and a value of 3.7 ev was assumed for ω Probability distribution of ω min derived from numerical simulations, in the case of a random electron capturing process, with ω allowed to vary with uniform probability in the range [3-4.5] (Leftpanel)-Probabilitydistributionofω min derivedfrommonte Carlo simulations, using the preliminary trap measurements as the input distribution and fixing ω to a value of 3.7 ev, recovered within ±0.1 ev in only 14% of the cases. (Right panel) - Deviations of this probability distribution from the one obtained from the case of a random electron capturing process Probability distribution of ω min derived from Monte Carlo simulations, fixing ω to a value of 3.7 ev, and reducing the uncertainties of the preliminary trap measurement by error scaling factors. (Left panel) - Scaling factor of 0.8: the ionisation energy parameter is recovered within ±0.1 ev in 21% of cases. (Right panel) - Scaling factor of 0.5: the ionisation energy parameter is recovered within ±0.1 ev in 87% of cases

20 xviii 3.13 (Left panel) - The modified χ 2 minimisation procedure does not reliably estimate the value of the ionising energy parameter using the set of trap measurements after the exclusion of the data with poor fits has been applied. The technique returns a series of local minima of similar significance in the investigated range of parameter values. (Right panel) - Probability distribution of ω min from Monte Carlo simulations: the input ω guess value is not correctly estimated to a significant confidence level XRT CCD operational temperatures over a period of 6 years (Left panel) - Results of the analysis the trap measurements for the temperature selections of interval I1=[-61.0,-59.0]; the χ 2 minimisation was not successful, as it yielded several minima of approximately the same magnitude within the range of ω values under investigation. (Right panel) - Distribution of the errors in the trap depth measurements with no temperature selection applied (black histogram, median error of 2.1 ev) and with the selected interval I1=[-61.0,-59.0] (red histogram, median error of 2.9 ev). The larger errors are a consequence of the lower photon statistics after the temperature filtering was applied (Left panel) - Results of the analysis for data selected within the temperature interval I2=[-62.0,-58.0]; ω min = 3.72 ev was found through χ 2 minimisation. (Right panel) - Ordered sequence of trap energy losses from the selection of the best quality fit. The horizontal red lines are spaced by ω = 3.72 ev Historical analysis of the energy losses. (Left panel) - The evolution of the measured energy in the pixel just below the known trap shows two large drops as new traps in the same column appear below its location. (Right panel) - The energy in the pixel just below the known trap is stable since the trap onset

21 xix 3.18 (Left panel)-χ 2 minimisationfromthesampleofdamagedpixels detected in 2008 and 2009, with error measurements of less than 2.5 ev, resulted in anω value of 3.73 ev. (Right panel) - Ordered energy loss values; four pixels with losses greater than 120 ev are not shown in the plot to better evidence the comparison of the measured trap losses with the steps of 3.73 ev (in red) expected based on the ionisation energy value obtained from the χ 2 minimisation procedure (Left panel) - Spectrum extracted from column RAWX=333 of the MOS1 central CCD from the entire 15 year dataset of calibration source observations. (Right panel)- Deep defect detected in column RAWX=69 in pixel RAWY=103 from the analysis of the calibration source data collected during year Distribution of trap energy losses measured in the MOS1 central CCD, from the analysis of 2005 and 2011 calibration source data (Left panel) - χ 2 minimisation results from the combination of the 2005 and 2011 samples of trap measurements. (Right panel) - Ordered sequence of trap energy losses from the combined 2005 and2011samples. The horizontal redlines arespaced by ω min = 3.51 ev Proton beam line at the tandem accelerator facility, from Ambrosi et al. (2005). Protons are scattered by the scattering foils to create a uniform irradiation of the CCD mounted on the sample plate Irradiation doses on the CCD. The left-hand area of the detector was exposed to a dose MeV protons cm 2, the righthand area was exposed twice, for a total dose of MeV protons cm The test facility where the proton damaged CCD, mounted within the cryostat and cooled to the selected temperature was illuminated with X-rays of different energies generated with the soft X-ray source and the high energy KEVEX source

22 xx 4.4 Measurement of the charge losses in the damaged pixel (16,160) in the titanium dataset at 75 C. The measured energies of the titanium Kα line above and below the damaged pixel are fitted with two quadratic functions and the trap depth is calculated as the difference of the fit values in the damaged pixel and the pixel just below it. The decline in the slope above (16,160) is likely caused by additional charge losses in pixels above the large trap Distribution of trap depths measured using titanium Kα X-rays at the CCD temperature of 75 C Deep manufacturing defect in column 134, evidenced from the analysis of a pre-irradiation dataset. The X-ray events detected in 20 adjacent pixels were merged, and the measured energy of the titanium Kα line was fitted by a Gaussian plus a constant function. In the figure, the energy at coordinate DETY is derived from merging X-ray photons detected in pixels [DETY-19, DETY] Map of the deep defects, in detector coordinates. Black stars - Pixels with charge losses E 50 ev at 75 C, identified in preference in the left area of the detector, exposed to the lower proton dose; Red stars - Pixels with charge losses E > 50 ev, mostly present in the right CCD region of higher proton dose; Blue triangles - Manufacturing defects A set of damaged pixels that presents no change in charge losses at different CCD operational temperatures. Examples shown here are from pixels (262,550) and (291,542) Examples of damaged pixels with a continuous increasing depth at colder temperatures (pixel (410,159), top panel), defects with a flattening of the charge losses below 75 C (pixel (225,496), central panel), and a drop in trapped charge at the highest temperatures (pixel (541,451), bottom panel) Examples of pixels with an energy dependence of the charge losses showing a steep increase up to the silicon energy and a flattening at the higher energies; this behaviour is seen in the majority of damaged pixels

23 xxi 4.11 Electron capture probability calculated as a function of the charge cloud density for trap types of cross section between σ = cm 2 and σ = cm 2, for a CCD22 operated at T= 75 C; the full circles are the capture probability values for electron clouds of oxygen Kα (524.9 ev), copper Lα (929.7 ev), silicon Kα (1.740 kev), titanium Kα (4.510 kev) and iron Kα (6.404 kev) fluorescence lines Examples of pixels presenting a flat energy dependence, typical of the shallower traps. In density models these traps are characterised by high capture cross sections resulting in a high electron capture probability even for low density charge clouds The trap depth in a set of pixels resembles a step function with respect to energy; this effect can be explained in density models by traps with very low capture cross sections, so that only high density packets lose a substantial fraction of electrons Comparison of the X-ray event energies columns with traps in datasetswithanaverageflux of1event per frameper column(in black) and 3 times higher (in green). Examples from columns 304and559areshownhere. Thereisanapparent fasterrecovery of the event energies in datasets of higher flux Sacrificial charge effect in column 410, that presents a deep trapping centre in pixel (410,159). In the top panel only the first X-ray events above the trap are plotted in black. These events are the sacrificial charge packets, and their charge is partially lost when captured by the trap. In the bottom panel, the X-rays following the SCPs above the trap are plotted in red. These X- rays are transferred through the damaged pixel with no loss of charge, as the trap levels remain filled while the frame is being readout. The data were taken using the silicon source at T= 75 C

24 xxii 4.16 EnergyofthesiliconKαSCPX-rays(inblack)andofthefollowing X-ray events (in green), fitted with a Gaussian function along columns affected by traps in pixels (119,293) and (410,159). The energy profiles, from the dataset taken at T= 75 C, show that electrons from the charge cloud of the first X-rays transferred over the damaged pixel are captured by the trap, while the following X-rays are transferred without losses In pixels (159,400) and (501,372) the effect of the sacrificial charge is reduced, and the energy of the following X-ray events is only partially recovered. For these damage clusters the detrapping time appears faster than for other traps in the CCD The sacrificial charge effect is also detectable for the datasets withthe standardflux of 1event per column per frame thatwere collected for the temperature and energy dependence analysis. The results for column 410 at T= 75 C are shown here, with SCP events in black and the following X-rays in red. Events at 4700 ev are from the titanium Kβ line Sacrificial charge effect for a sample of typical traps (from left to right, pixels (99,293), (261,280), (323,381)) as a function of temperature (from top to bottom, T= 100 C, T= 75 C, T= 55 C, T= 50 C). The energy profiles of the SCPs are well separated from the profiles of the X-ray events following the SCPs for the coldest temperatures, but become indistinguishable at 50 C Partial reduction of the energy lost to the traps in column 132 (a manufacturing defect, left panel) and column 410 (damage caused by protons, right panel). For X-ray events detected further up above the trap in a column the number of electrons lost in the damaged site is reduced. The observed behaviour is shown here at three temperatures, 75 C, 65 C and 55 C. A binning of 20 pixels was used to generate the energy profiles Fits to the recovery profiles for a sample of proton-generated traps in columns 410, 421, 560 and 593, from the titanium dataset at 75 C. The profiles have been extracted using a 5 pixels binning

25 xxiii 4.22 Detrapping time as a function of temperature derived from the fits to the recovered energy profiles for the trap in column 410. As expected, the emission time is higher at colder temperatures Temperature dependence of two manufacturing defects in pixels (132,104), left panel, and (317,114), right panel, showing a flattening of the damage depth at colder temperatures or an additional drop at 100 C Energy dependence of two manufacturing defects in pixels(133,255), left panel and (360,179), right panel, displaying a curved dependence on X-ray energy, with a flattening at titanium and Iron The sacrificial charge effect for manufacturing trap (555,465), in the dataset taken at T= 75 C, with SCP events in black and the following X-rays in red (left panel), and the profiles derived by fitting the energies along the columns by a Gaussian plus a constant function (right panel) Energy profiles of the SCPs and the following X-rays in column 355, extracted for datasets taken at 75 C (left panel) and 50 C (right panel). While the profiles are well separated at 75 C, at 50 C the energies overlap within the uncertainties, revealing an emptying of the trap energy levels on timescales of the order of a few tens of row transfers at the warmer temperature Manufacturing defects show evidence of spreading over 2 or more pixels, as in the case for the damage in column 132. On the left panel, the measured energy of the X-ray events detected in rows 102, 103 and 104 are plotted in black, blue and red, respectively, and show an initial loss of 100 ev in row 103 followed by an additional loss of 300 ev in pixel 104. The spreading of the trapsovertwopixelscanalsobeseenintheenergyprofilederived with 1-pixel binning (right panel)

26 xxiv 5.1 (Left panel) - Map of protons with energies above 0.1 MeV derived for the Swift orbit over a period of one day using SPEN- VIS s online tools; most of the proton dose is accumulated during the SAA passages. (Right panel) - Flux of E p 0.1 MeV protons as a function of time over a period of 1 day, with peaks corresponding to the times spent by the spacecraft in the SAA, as derived by SPENVIS Integral and differential spectrum of the trapped protons in the Swift orbit averaged over a one day period, derived using SPEN- VIS Technical drawing of the XRT aluminium shield (drawing E- SWT-9748, Lower proton shield ) Integral and differential flux of the protons (top panel) and secondary neutrons (bottom panel) behind 18.5 mm of aluminium shielding, derived through Monte Carlo simulations using SPEN- VIS D model of the XRT shield, designed using the Geometry Generation Tool software, as seen from two different viewing angles. The shield is modelled with a series of aluminium cylinders, and the detector, inside the central hollow cylinder, modelled as a silicon box The Frascati Neutron Generator Schematic picture of the Frascati Neutron Generator, from Martone et al. (1994) Photographs taken during the neutron irradiation experiment at the FNG. The tritiated-titanium target (left panel); positioning of the CCD for the irradiation (central panel); the target and the CCD pictured before the irradiation (right panel) Normalised energy distribution of the neutrons irradiated on the CCD at the Frascati Generator, obtained from the Monte Carlo N-particle transport code in use at the facility

27 xxv 5.10 The comparison of the energy response along the detector columns in datasets taken before (in blue) and after (in black) the CCD exposure to neutron irradiation allows the identification of neutron damaged pixels affected by large energy losses. Column 37 is affected by a neutron damaged pixel in row 284 that causes energy losses of approximately 60 ev; the damaged pixels in columns 73 and 90 present evidence of the recovery effect discussed previously for a sample of proton damaged pixels (Section 4.9); column 193 is another interesting example, with a newly generated neutron damaged pixel in row 488, and a previously identified proton damaged pixel in row Distribution of the energy losses of protons traps(in red) and the neutron traps (in blue) identified and measured at T= 75 C at the titanium Kα energy line of kev Map of the neutron generated defects, in detector coordinates, with pixels with charge losses E 50 ev at 75 C in black and pixels with charge losses E > 50 ev in red. The defects are uniformly distributed over the detector, but the shallower traps are more securely and easily identified in the left region of the CCD, exposed to the lower dose of protons, that presents an overall lower CTI One class of neutron damaged pixels shows a flat CCD temperature dependence of the measured charge losses, as in the cases of pixels (53,516) and (420,517) presented here. In these pixels, the thermal noise does not increase substantially at the warmer regimes, resulting in a similar number of unfilled trapping sites in the damaged location over the investigated CCD temperatures The effect of temperature on the measured energy losses in neutron generated traps. Pixel (203,296), top left, shows a continuous reduction of the energy losses as the temperature is increased; in pixel (69,552), top right, the amount of lost charge flattens for T 75 C; in pixels (255,94) and (95,174), bottom panels, a reduction of the energy losses is measured for T= 100 C as the trapping sites become partially frozen

28 xxvi 5.15 In a handful of cases the measured temperate dependence is reversed with respect to what is more commonly observed, with energy losses reduced when the CCD is operated at colder settings. This effect can be explained by long electron release times, characteristic of the deeper traps of energy levels of E t 0.40 ev Energy dependence of neutron traps. (Left panel) - Difference in charge losses measured at titanium (D(Ti), KeV) and silicon (D(Si), kev). (Right panel) - Fraction of charge losses at silicon compared to titanium measurements Sacrificial charge effect of neutron damaged pixels in titanium datasets at T= 75 C, in columns 472 (top panel) and 505 (bottom panel). The SCP photons are plotted in black, the following X-ray events are in red; the SCP events are exposed to the capture by all the trapping sites in the damaged location, while the following X-rays charge packets are only partially affected as most trapping levels are occupied by the previously captured electrons EnergyprofilesfromthetitaniumKαemissionlineatT= 100 C of thescp (in black) andthe following CP (ingreen) incolumns 33 and 69. The difference in the profile energy shows that the trap energy levels remain filled between the transfer of the sacrificial and the following X-rays. This behaviour is observed for all the investigated trapping sites at T= 100 C. The release timescale is therefore longer than the transfer time of a few tens of rows and hundreds of rows for traps located close to the top and the bottom of the CCD respectively. From these emission timescales, theupperlimitsofe t 0.28eVandE t 0.32eVfor traps close to the top and the bottom of the CCD are estimated Sacrificial charge analysis of column 37 as a function of temperature, with the energy profiles from the titanium Kα emission line of the SCP in black and the following CP in green. The SC effect is not detected at T= 60 C (left panel), while it is clearly observed at the colder T= 75 C (central panel) and at T= 100 C (right panel) settings, as the trap release time scale becomes longer

29 xxvii 5.20 At T= 60 C, in approximately half of the damaged pixels under investigation, the amplitude of the sacrificial charge effect is reduced along the column, as the trapped charge is released between the capture of the sacrificial CP and the transfer of the following CP. These defects have trap energy level of the order of E t = 0.36±0.02 ev The sacrificial charge effect at T= 50 C is observed in only a small fraction of the detects, and its amplitude is reduced over a few tens of row transfers, as shown for column 109 (left panel) and 156 (right panel) Fits of the recovery profiles in column 69 from the titanium datasets at T= 75 C (left panel) and T= 60 C (right panel). The derived emission time is longer at the colder setting, as expected from the temperature dependence of Equation Comparison of the observed and trap corrected spectra extracted from PC mode observations of the Tycho SNR taken in September 2013; the implemented corrections improves the spectral energy resolution of the instrument, enhancing the stronger lines and recovering the weaker lines that could not be resolved in the uncorrected spectrum

30 Chapter 1 Introduction 1.1 Overview The performance of a Charge-Coupled Device (CCD) flying on a satellite mission is affected by its exposure to space radiation. High energy particles that interact with the CCD cause the displacement of silicon atoms and the development of defects in the lattice structure (Srour & McGarrity, 1988). The defects can act as charge generation centres (hot pixels), increasing the camera background noise and lowering its sensitivity, or as charge traps, degrading the image quality of optical CCDs and the energy resolution in X-ray detectors. These negative effects have been seen in all the satellite missions using CCDs, in some cases with unexpected and unfortunate severity; the Chandra X-ray observatory, for example, experienced a rapid degradation of the spectral resolution of the Advanced CCD Imaging Spectrometer (ACIS) front illuminated CCDs during the first month of the mission (Prigozhin et al., 2000). Investigations pointed to the cause of the damage being soft protons from the Van Allen belts that could penetrate the telescope mirrors and reach the detectors. Further damage was prevented by moving the camera to a protected position during the subsequent spacecraft passages through the radiation belts. The science requirements of satellite missions pose stringent constraints on image quality and energy resolution. ESA s Gaia mission, for example, is creating the most complete map of the Milky Way through repeated measurements of the position of over a billion stars to derive their parallaxes and proper motions. Gaia s performance will depend on its ability to read out the CCD 1

31 2 images effectively, with minimal loss of signal due to Sthe defects generated by radiation damage. In the planning phase of a satellite mission with a CCD on board, dedicated space radiation software such as ESA s SPENVIS (the Space Environment Information System 1 ) is used to model the radiation environment to which the spacecraft will be exposed. The total dose absorbed by the CCD is derived for different configurations, such as the orbit altitude and inclination, or the thickness and the material of the proton shield, to predict the loss in spectral resolution as a function of time after launch. These models provide the optimal design parameters in terms of shielding and CCD operational parameters (like the detector cooling temperature and image clocking time) in light of the mission requirements to minimise the effects of radiation. The design of the CCDs has undergone important developments since their invention to improve the radiation hardness of these detectors. For example, notch channel technology has been introduced to reduce the effects of radiation damage. The notch channel narrows the volume of the signal charge collection and transfer, reducing the number of defects generated by radiation that interact with the charge cloud. The object of this thesis is the study of the radiation damage of the X-ray telescope (XRT; Burrows et al. (2005)) CCD. The XRT is one of three scientific instruments on board the Swift satellite (Gehrels et al., 2004), launched into a low Earth orbit on 20 November 2004 with the main goal of studying gamma-ray bursts (GRBs). The energy resolution of the detector has gradually degraded during the life of the mission due to the effects of radiation. The XRT CCD damage is studied by analysing the datasets continuously collected on board the instrument from four radioactive calibration sources, and from datasets consisting of observations of supernova remnants, astronomical X-ray sources with strong emission lines. I have developed dedicated software to identify the pixels of the XRT CCD affected by traps and to measure the amount of captured charge. This information is included in specific calibration products and is used by the Swift software to model out charge losses and partially recover the energy resolution, improving the quality of the XRT data. The datasets from the radioactive sources are also used to investigate 1

32 3 the dependence of the charge traps on the CCD operating temperature and the incoming photon energy and in the derivation of the fundamental parameter ω of silicon, the photon energy required to generate an electron-hole pair by photo-absorption. A surprising outcome of the trap analysis has been the discovery of highly damaged pixels with charge losses of the order of 100 ev or more. This is unexpected if the damage is caused by protons, that are believed to displace a limited number of silicon atoms in their interactions and to generate one or two defects in a pixel, with a resulting charge loss of one or two electrons. This result raised the possibility that secondary neutrons from the telescope body and the camera aluminium shield could instead be responsible for the traps seen in the most damaged pixels, as neutral neutrons directly interact with the silicon nuclei without mediation of the Coulombic repulsion as in the case of protons. This unexpected result was investigated in a laboratory program at the Space Research Centre (SRC) at the University of Leicester to characterise the damageofane2vccd22, similar totheccdonboardswift, thatwasexposed to 10 MeV protons at the Tandem accelerator facility at Harwell, Didcot (UK), in The CCD operating temperature, energy and X-ray flux dependence of the charge traps were measured by uniformly illuminating the camera with X-rays of chosen energy and intensity, allowing the derivation of important trap physical parameters as their energy level and size, that pointed to the nature of defects generated by the irradiation. The CCD was then taken to the Frascati Neutron generator and exposed to 14 MeV neutrons, and the damage analysis was repeated at the SRC Camera test facility to characterise neutron induced defects. The results of the laboratory program are discussed in this thesis and are compared to the properties of the damage seen on board the XRT and to other X-ray missions. The outcome of this work can provide valuable insights for the design of radiation shields of greater protecting effect for future satellite missions in a low Earth orbit using CCDs or to missions like Gaia where the modelling of the effects of charge traps are of fundamental importance to achieve its scientific goals.

33 4 1.2 Charge Coupled Devices Charge coupled devices have been flown on all the latest X-ray satellite missions, for the first time on ASCA, a Japanese mission launched in 1993, and then on Chandra, XMM-Newton and more recently on board Swift and Suzaku. CCDs have been the detector of choice for these missions as they provide an ideal combination of good spectral and spatial resolution, high quantum efficiency, low background noise and large dynamic range. Their use for scientific applications and in astronomy in particular is presented in great detail in various publications (Mackay, 1986; Janesick, 2001); here I provide a short introduction of their properties and operations. The basic unit of a CCD is the metal oxide semiconductor (MOS) capacitor, consisting of a doped 2 silicon substrate, a silicon dioxide (SiO 2 ) layer and a deposited conductive gate (metal or polycrystalline silicon) as schematised in Figure 1.1. When a positive voltage is applied to the gate it leaves a region in the silicon depleted of mobile carriers, the depleted region. It is in this region that the signal charges generated by the absorption of an optical or an X-ray photon by silicon are collected, and in particular at the silicon-silicon dioxide interface, at the maximum of the potential well. This configuration presents an important drawback, as the signal electrons have a high probability of being captured by the many trapping sites formed at the silicon-silicon oxide interface. For this reason, early on in the development of CCDs the buried channel technology was invented, with the introduction of a thin n-doped region between the oxidised surface and the p-doped silicon substrate. The presence of the n-region modifies the potential well, shifting the volume where the signal electrons are collected away from the surface, in the buried region (Figure 1.2), thus avoiding the trapping sites. 2 The doping process consists in the introduction of impurities in the semiconductor crystal. Silicon, a group IV semiconductor with four electrons in the valence band can be doped with group V atoms such as phosphorous, with five electrons in the valence band, or group III atoms (three electrons in the valence band) such as boron. A group V atom is called a donor, as its introduction adds extra free atoms to the crystal valence band, increasing the semiconductor conductivity. A group III atom, called an acceptor, leaves one of the silicon valence electrons with a broken bond, a hole. The addition of donors is referred to as n-doping, adding acceptors is p-doping.

34 5 Figure 1.1: The metal oxide semiconductor (MOS) is the basic unit of a CCD. The gate electrode is deposited on top of a doped silicon substrate with an oxidised surface (SiO 2 ). Figure 1.2: Buried channel potential well. The introduction of a thin n-doped region shifts the minimum of the potential in the silicon away from the interface with the oxidised surface, where trapping sites can capture signal charges. Figure from Janesick (2001).

35 Electron-hole generation Signal charge is generated by the interaction of photons in silicon through the photoelectric effect. In silicon, the gap between the valence band (the highest energy band in which electrons are bound to the atoms) and the conduction band(the energy band where electrons are free to move within the lattice structure) is approximately 1.14 ev. Photons in the far infrared, with energies below 1.14 ev pass through the CCD without generating charge. An optical photon instead has enough energy to be photo-absorbed, an electron is promoted to the conduction band and a vacancy (a hole) is created in the valence band. The electron-hole (e-h) pair created is free to move in the lattice. Single photons of higher energy E, for example X-ray photons, when photoabsorbed generate on average a number of e-h pairs N N = E ω (1.1) where ω is a weak function of temperature, approximately equal to 3.63 ev/e- at room temperature (Pehl et al., 1968). For example, the absorption of a 5890 ev Mn-Kα X-ray photon generates on average 1620 e-h pairs. In more detail, the photo-absorption of a 5890 ev X-ray by silicon occurs in the K-shell with 92% probability (Cromer & Liberman, 1970), and an electron is emitted with kinetic energy E = 5890 E b, where E b =1740eV is the K-shell binding energy. The ejected electron interacts through inelastic collisions with other orbital electrons generating a trail of e-h pairs. The empty K-shell is then filled by an electron of an outer shell and one of two processes can occur: an Auger cascade, as electrons are ejected from the atom, or the emission of a silicon fluorescence X-ray. In the first case, the electrons from the e-h trail and from the Auger cascade add up, producing the primary photo-peak. If instead the silicon X-ray is emitted, it can leave the region of interaction and the escape peak line is produced at an energy E b below the photo-peak, as only 1140 electrons are liberated [N = (E E b )/ω = ( )/3.63]; if the silicon Kα X-ray is eventually absorbed it generates 480 electrons (N = E b /ω = 1740/3.63), producing the fluorescence line, otherwise it leaves the CCD undetected. The lines discussed here can be seen in Figure 1.3, for the XRT CCD illuminated by a 55 Fe source producing an input spectrum consisting of Mn-Kα (5890 ev) and

36 7 normalized counts s 1 kev Si Fluorescence Peak Si K escape peaks Mn Kα Mn Kβ Primary Photopeaks Mn Kα Mn Kβ Energy (kev) Figure 1.3: Spectral redistribution function, showing the primary photo-peaks, the escape peaks and the fluorescence peaks, from an input spectrum generated by the XRT 55 Fe door source shortly after launch, producing Mn-Kα (5890 ev) and Mn-Kβ (6490 ev) lines. Mn-Kβ (6490 ev) lines; the spectrum was acquired shortly after launch before the telescope door was opened and the 55 Fe source moved out of the field of view Charge collection and transfer The charge cloud generated by the photo-absorption of an X-ray moves under theinfluenceofanelectricfield. TheXRTdetectorisathreephaseCCD,where the electrodes, consisting of polycrystalline silicon, are connected in triplets. One pixel dimension is therefore defined by the width of the three electrodes,

37 8 while the second surface dimension is determined by the presence of heavily doped channel stops, that prevent the charge from spreading laterally. During a frame exposure, the signal charge is collected in the depletion region under the central electrode of a pixel, kept at a high voltage, while the neighbouring electrodes are at 0 V. Charge is then moved (coupled) from under one electrode to the next by alternating the electrodes kept at high voltage, as illustrated in Figure 1.4. Charge is thus transferred through the rows of the detector. In the XRT CCD (described in more detail in Section 1.3) the last row of the image section is transferred to the frame-store section, that is not exposed to X-rays, and then to the one dimensional serial register, and finally read out through the output amplifier, where the charge from each pixel of the original image is measured. Charge transfer is not a perfect process; the fraction of charge lost in the transfer between two pixels is a parameter called charge transfer inefficiency (CTI). The charge transfer efficiency (CTE) is defined as 1-CTI Noise There are multiple sources of noise in CCD detectors, related to the charge generation and transfer, to the dark current and to the charge readout. Their origins are summarised and quantified below. The variance σ 2 on the number of electron-hole pairs generated by the photo-absorption of an X-ray photon is not a purely Poisson process, but can be described through the Fano factor F (Fano, 1947), F E σ generation = (1.2) ω where E is the incoming photon energy and ω is the energy required to generate an e-h pair. The Fano factor is equal to F = 0.118±0.004 at 5.9 kev at 120 K (Lowe & Sareen, 2007), that results in a value of σ generation = 13 electrons at that energy. Transfer noise is related to the charge lost to traps during the readout process. Electron capture by the traps is a stochastic process, its variance is a function of the charge transfer inefficiency,

38 Figure 1.4: Charge transfer in a pixel in a three phase CCD; the electrons move under the electrodes kept at high voltage. 9

39 10 σ transfer = CTI N p n (1.3) where N p is thenumber ofpixel transfers andnis thenumber of electrons in the signal. For a CTI value of 10 5 the value of σ transfer is approximately equal to 5 electrons for a charge packet of E=5.9 kev transferred over 300 pixels. Thermal generation of charge carriers causes intrinsic dark current in semiconductors. In CCDs, the major sources of dark current are the imperfections and impurities at the silicon - silicon oxide boundary that introduce spurious energy levels between the valence and conduction bands. Thermally excited electrons are first promoted to these intermediate energy levels before reaching the conduction band. Hot pixels are pixels with high level of dark current caused by imperfections or impurities in the silicon lattice. To reduce the level of dark current and the number of hot pixels CCDs are often actively cooled, resulting in typical levels of dark current below 1 electron rms at 100 C. Additional noise is generated by the preamplifier at the output node. The mean readout noise of an e2v 3 CCD-22, such as the one on board the XRT is approximately 4 electrons rms at 100 C (Ambrosi et al., 2005). These sources of noise all contribute to limit the detector sensitivity and the spectral resolution. The broadening of the spectral line is therefore described by a variance σ 2 given by σ 2 = σ 2 generation +σ 2 transfer +σ 2 dark +σ 2 readout (1.4) 1.3 The Swift X-ray telescope The Swift mission is an international collaboration led by NASA in association with Universities and Research Institutes in the United States, in the United Kingdom and in Italy with the main goal of studying celestial gammaray bursts (GRBs). The low Earth orbiting Swift s main scientific strengths are its unique ability amongst astronomical satellites to perform rapid and autonomous manoeuvres to point its telescopes to the desired sky coordinates and its multiwavelength coverage with the Burst Alert Telescope (BAT, Barthelmy et al. (2005)), a coded mask, wide field of view Gamma-ray telescope, the 3

40 11 Figure 1.5: Diagram of the X-ray Telescope (Burrows et al., 2005). X-ray telescope (XRT) and the UV-optical telescope (UVOT, Roming et al. (2005)). Swift has discovered hundreds of GRBs, as well as other fascinating astronomical events such as the tidal disruption of a star by a supermassive black hole (Burrows et al., 2011) and the outburst of a new soft gamma-ray repeater (Kennea et al., 2013), detecting the events in the γ-ray band with the BAT and following the aftermath of the outbursts with its narrow field telescopes at lower energies. The XRT is a grazing incidence Wolter I telescope. A functional diagram of thexrtisshowninfigure1.5andanexplodedviewisillustratedinfigure1.6. The mirrors are the flight spare of the JET-X mission. The mirror module has 12 concentric gold-coated nickel shells with focal length 3500 mm. The mirror moduleiskept atacontrolled, warmtemperatureof20±0.5 Cby tubeheaters and a thermal baffle, to prevent distortions of the shape of the point-spreadfunction (18 arcsec Half Power Diameter at 1.5 kev). The effective area is 110 cm 2 at 1.5 kev. The X-rays are focused on an e2v CCD-22, the same type of detector flown on the XMM mission in the EPIC MOS cameras (Turner et al., 2001), with a field of view of 23.6 x 23.6 arc-minutes. The CCD is housed in the Focal Plane Camera Assembly (FPCA). An aluminium shield protects the CCD from space radiation, in particular from proton damage. Optical light is blocked by a filter

41 12 Figure 1.6: Exploded view of the XRT; figure from the PSU XRT website 4. consisting of a single 1840 Å fixed polyimide film coated on one side with 488 Å of aluminium. A 55 Fe calibration source is placed on the FPCA door and was used during pre-flight testing and shortly after launch, before the door was opened when the source moved outside the field of view. A second set of four 55 Fe sources continuously illuminate the corners of the detector and are used in orbit to monitor the spectral resolution and to measure the gain and the charge transfer efficiency. A simplified schematic view of the CCD-22 is shown in Figure 1.7 A Thermo-Electric Cooler (TEC) coupled with a thermal radiator was designed to cool the camera to a fixed temperature of 100 C, to limit dark current and the effects of radiation damage. The power supply to the TEC failed shortly after launch, leaving the TEC inoperable, and the CCD now relies on the radiator passive cooling with operating temperatures typically ranging between 70 C and 55 C, depending on the orbital parameters and the spacecraft pointing configuration. The XRT temperature management is part of the attitude planning of the satellite (Kennea et al., 2005). The XRT detector is a front illuminated, three-phase frame transfer CCD that utilises high resistivity silicon and an open electrode structure to improve the quantum efficiency at low energies. The energy range is kev and the energy resolution at launch was 135 ev at 5.9 kev FWHM. In-depth descrip- 4

42 Figure 1.7: Schematic view of the CCD-22 on board the XRT. In the image region, the field of view is defined by the structure of the optical filter housing; four 55 Fecalibrationsourcesilluminatethecornersoftheimagesection. Images are transferred from the image to the covered store section, and readout through the serial register to the output amplifier. 13

43 y (micron) 20 OE F OE 0.8 etched electrode 10 0 F z (micron) Si 3 N x (micron) 0.2 SiO 2 epitaxial Si Figure 1.8: XRT CCD-22 SEM images. (Left panel) - Top view of the pixel, showing the open electrode surface (OE) and the electrode finger (F) (Right panel) - Side view of the pixel, showing the etched electrode layer, the nitride, oxide and epitaxial silicon layer. tion of the XRT spectroscopic performance during the first years of the mission is found in Godet et al. (2007). The spectral resolution (Equation 1.4) since launch has degraded because of the higher than expected operational temperature after the failure of the TEC power supply and of the build-up of charge traps caused by space radiation, described in detail in the next section, that lowered the charge transfer efficiency. TheCCD cm 2 imagingareaconsistsofa arrayof40 40µm 2 pixels; the pixel scale is therefore 2.36 arcsec/pixel. The store frame is an array of pixels of dimension µm 2. The CCD-22 structure is described in detail in Godet et al. (2009); in Figure 1.8, images of the pixel structure obtained by a scanning electron microscope provided by e2v are presented, showing the open electrode structure (left panel) and the electrode, the silicon nitride, the silicon oxide and epitaxial layers (right panel). A micrometeorite event (Abbey et al., 2005) in May 2005 hit the detector causing the appearance of hot pixels and several hot columns, now masked onboard. The CCD can currently be operated in two modes, Photon Counting (PC) and Windowed Timing (WT). Images in detector coordinates from GRB observations taken using the two modes are shown in Figure 1.9. PC mode is the traditional CCD imaging mode, it provides a 2-D spatial information with

44 15 Figure 1.9: XRT operational modes. Images in detector coordinates of two point sources, observed in Photon Counting mode (PC, left panel), and Windowed Timing mode (WT, right panel). PC mode is used for fainter sources, and provides 2D spatial information; WT mode has better timing resolution at the expense of spatial information, and is used for brighter targets. 2.5 s time resolution, and is typically used in observations of faint sources. The WT mode readout window consists of the central 200 columns of the detector, it provides 1-D spatial information and 1.8 ms timing resolution and is best suited for bright sources XRT data format and processing XRTdatasets consist of lists ofx-rayevents stored infits files format, the event files. Unfiltered event files include all the events telemetered to the ground by the spacecraft. These files are processed by the xrtpipeline applying specific filters and selections such as hot pixel removal and event grade selection using calibration files stored in the Swift CALDB to generate a cleaned event file of genuine and calibrated X-ray photons. In the unfiltered event files, each X- ray event is recorded with the following information: arrival time, CCD frame number, detector coordinates and energy; in PC mode, the energy of the pixels in a 3 3 pixel grid is stored, while in WT mode a 1 3 box is used, including the central pixel and the left and right adjacent pixels. In the xrtpipeline processing, the CCD hot columns and hot pixels (in PC mode) are first identified and removed and sky coordinates are assigned

45 16 to the X-ray events. The energy grids are used to assign a grade to each event using pre-defined energy thresholds. When an X-ray photon is detected a charge cloud is generated whose volume can remain confined to a single pixel or can spread to two or more pixels if, for example, the photon was absorbed at the edge of a pixel or in the field-free region of the pixel (as is generally the case for the high energy events). Cosmic rays, on the other hand, when passing through a CCD generate charge that is spread over multiple pixels. The grade assignment is therefore crucial to select only genuine X-ray events and to reconstruct the total energy of the photons. In PC mode, if only the central pixel hasanenergyabovethethresholdagrade0isassignedtotheevent (single pixel events). If the charge is above the threshold in some of the neighbouring pixels as well, a higher grade is assigned (split events). Only grade 0 to 12 events are considered real X-ray events, higher grades are discarded and not included in the cleaned event file. In WT mode, grade 0 events have the central pixel only above threshold, and grade 1 and grade 2 events (with respectively the left and the right pixel also above threshold) are also considered genuine X-rays and included in the cleaned event file. The successive pipeline task is the correction for the bias. In PC mode, the bias is automatically subtracted on-board using the bias map recorded during the spacecraft slews, but a dynamical correction applied on the ground is necessary to take into account the effects of temperature and other artifacts such as optical contamination. The residual time and position dependent bias level is estimated from the corner pixels of the energy grid of single pixel events by averaging their values and is subtracted to the bias map measured on board. Finally, the energy of the X-rays in ev is derived using the CALDB gain files. The calculation includes corrections for the charge losses caused by the average CTI of the CCD and specific, position-dependent losses caused by the most radiation-damaged pixels. These energy losses and corrections are at the heart of this thesis and will be discussed at length in detail in the following chapters.

46 Radiation damage Space radiation environment A satellite in space is exposed to a severe radiation environment that depends on its orbital altitude, inclination and eccentricity. Spacecrafts beyond the Earth s magnetosphere, for example, are exposed to the protons, electrons and alpha particles from coronal mass ejections and solar flares. The Earth s magnetic fieldextendsto 90,000kmonthesunwardside, wherethesolarwindabruptly slows down (the bow shock); this field acts as a shield, deflecting approximately 99% of particles directed towards Earth. The minority of particles from the solar wind that penetrate the magnetosphere can become trapped by the Earth s magnetic field in the Van Allen belts. The outer belt extends to 60,000 km and mostly consists of trapped energetic electrons (of energies between 0.4 and 7 MeV), while the inner belt is typically located between 600 and 25,000 km (Ganushkina et al., 2011) and is composed of both electrons and protons (with energies from 0.4 to 500 MeV). The trapped particles can pose a serious threat to the spacecrafts detectors, as already discussed for the unfortunate case of the Chandra X-ray mission. Galactic and extragalactic cosmic rays are an additional source of space radiation. They consist of very high energy charged particles, protons, alpha particles and heavier nuclei, originating outside the solar system, in supernovae explosions (Ackermann et al., 2013) and possibly in Active Galactic Nuclei. Cosmic rays can reach energies of ev and their flux in the upper atmosphere is dependent on the solar cycle, peaking during solar minimum. Swift, in a low Earth orbit at 600 km altitude with an inclination of 20, is protected from the solar wind by the Earth s magnetic field. The main sources (>99%) of radiation are the passages through the South Atlantic Anomaly (SAA), the geographical area above South America and the Atlantic Ocean where the Earth smagnetic field isweaker andtheinner VanAllenbelt extends closer to the Earth s surface (down to an altitude of 200 km). The total dose from the exposure to the proton environment in the SAA and the expected degradation of the detector response were modelled before the satellite launch (Short, 2000), predicting a spectral resolution within the mission requirement (FWHM< 300 ev at 6.4 ev 2 years into the mission) for a CCD cooled at

47 C Ionising radiation and displacement damage Radiation affects CCDs by means of two main mechanisms: ionisation damage and displacement (or bulk) damage. When a charged particle interacts with a CCD, approximately 99% of its kinetic energy is deposited in the CCD creating electron-hole pairs (Ionising Energy Losses, (IEL), McLean et al. (1989)). The remaining particle energy is instead deposited in interactions with the silicon nuclei (Nonionizing energy loss, NIEL), where collisions can displace the nuclei from the lattice structure, generating trapping sites (Holland, 1993). The amount of radiation received by a CCD can be expressed in terms of dose (used for ionising losses) and fluence (for bulk damage). The radiation dose D is defined as: D = de dm (1.5) where de is the energy deposited in a volume of the CCD of mass dm. Radiation dose is expressed in rad, 1 rad being the deposition of 100 erg in 1 gram of material, or in Gray, 1 Gy = 100 rad. As an example, considering that the energy needed to generate an electron-hole pair in silicon is 3.65 ev and the density of silicon is 2.32 g/cm 3, 1 rad of ionising radiation (γ-rays, X-rays, electrons) generates electron-hole pairs in volume of 1 cm 3 of silicon. The fluence is related to the dose by the stopping power S, S = de dx (1.6) where de is the energy (MeV cm 3 mg 1 ) deposited in a mass of depth dx (cm), so that D = KFS (1.7) where F is the fluence (particles/cm 2 ), S is the stopping power of the particle (MeV cm 2 mg 1 ) and K is a constant ( erg MeV 1 ). A higher stopping power causes larger energy losses and total doses. The stopping power of a proton in silicon has its maximum at a proton energy of 60 kev (defined as the Bragg peak).

48 19 Ionising energy losses are mostly due to high energy photons and electrons. In a semiconductor, the electron and hole pairs created by ionising radiation quickly recombine or are swept away by the applied voltages. Instead, in the gate insulator (the silicon dioxide layer in Figure 1.1), the fraction of the electrons and holes that do not recombine become trapped and generate a voltage (called flatband voltage ) that causes a shift in the channel potential. Permanent damage is also caused at the interface between silicon and the gate dielectric, where ionising radiation breaks the weak atomic bonds introducing interface states that act as trapping sites and as generator of dark current (Van Lindt, 1980). The charge buildup at the interface modifies the gate voltage parameters, potentially affecting the performance of the device. Displacement damage occurs in the silicon layer and is due to collisions of protons, neutrons and heavier ions with silicon nuclei that can displace an atom from the original position, generating an interstitial atom and leaving behind a vacancy. The interaction of high energy protons with the silicon atoms is Coulombic, and the collision typically displaces one or a few atoms. In CCDs, the most efficient protons for displacement damage have energy of 250 kev, at lower energies protons are absorbed by the gate and dielectric, while higher energy protons are more penetrating in silicon, and therefore have a lower chance of displacing silicon atoms in the volume where charge is collected and transferred. A neutron has a lower cross section compared to a proton ( cm 2 for1mevneutrons, fromthejanisdatabaseofnuclearreactiondata, compared to cm 2 for 1 MeV protons), so it displaces a smaller number of primary recoil atoms. On the other hand, neutrons directly collide with the nuclei and more energy is transferred to the recoil atom (an average of 75 kev compared to 20 ev in case of protons), that can in turn undergo many collisions within the lattice producing a large number of displacements, a defect cluster. A charged particle has a very low probability of undergoing a displacement interaction as almost all its energy is deposited through ionising losses. Kinchin & Pease (1955) estimate the average number of displaced atoms by the primary knock-on charged particle N PRI using a simple model based on a displacement threshold energy E D ( 20 ev for silicon) and the initial kinetic energy of the projectile E pro of mass M 1

49 20 N PRI = 1 2 [ 1+ln ( ΛEpro 2E D )] (1.8) where Λ = M 1 M 2 /(M 1 +M 2 ) 2, and M 2 is the mass of the target atom. The total number of displacements D T is then given by D T = N AσρN PRI χ path A (1.9) where N A is Avogadro s number, σ is the particle cross section, ρ is the density, χ path is the path length and A the atomic weight of the target. For an incoming 1 MeV proton the average number of displaced silicon atoms is 1.18 over a 1.5 µm path; for comparison, the number of displacements by a 100 MeV proton over the same depth is More detailed models based on the original work of Kinchin & Pease are used in Monte Carlo codes such as SRIM (Stopping and Range of Ions in Matter, (Ziegler & Biersack, 1985)) to simulate the bulk damage caused by different radiation sources. An example of the results of a SRIM simulation is shown in Figure When an atom is displaced it moves to an interstitial position, leaving behind a vacancy; a vacancy next to an interstitial is known as a Frenkel pair. The majority of vacancy-interstitial pairs generated by energetic particles recombine (in 98% of cases at room temperature (Van Lindt, 1987)). The interstitial atoms are not active and do not modify the properties of the device, but the vacancies are highly mobile and can form stable defects with dopants or other impurities in the semiconductor. For example, a vacancy can combine with doping phosphorous atoms, forming a P-V trap (also known as an E- centre), or with impurities such as oxygen atoms (the O-V trap, or A-centre). Two vacancies form a stable defect called a divacancy, three vacancies form a multivacancy defect. These defects generate spurious energy levels between the conduction and the valence bands, and have the ability of trapping electrons or of releasing charge. Each kind of trap has characteristic charge trapping and emission times, described by the stochastic processes of the Shockley-Read-Hall theory (Shockley & Read, 1952). According to the theory, an electron is captured by an empty trap in a time t with a probability P c given by

50 Figure 1.10: SRIM simulation of the number of displacements caused in a CCD by incoming protons perpendicular to the detector surface of energies between 250 kev and 1 MeV, from Janesick (2001). 21

51 22 P c = 1 e t τc (1.10) where the capture time τ c is τ c = 1 v t nσ (1.11) where v t is the thermal velocity of an electron, n is the number density of free electrons and σ is the electron capture cross section. The trapped electrons are released with an emission time scale τ e, τ e = 1 v t χn c σe Ec E t kt (1.12) where N c is the density of states in the conduction band, χ is the entropy factor, E c is the energy of the bottom of the conduction band and E t is the trap energy level below the conduction band. The emission time constant is an exponential function of the CCD temperature, its dependence is shown in Figure 1.11 for some common trap types. At cold temperatures, for the traps with high characteristic trap energy E t, once an electron is captured it is not released until a much later time: the trap is effectively frozen, and other electrons can be transferred safely through the trapping site without being captured. Cooling the CCD is therefore a powerful way to reduce the effects of radiation damage, limiting dark current and charge transfer inefficiency. On the other hand, raising the CCD temperature to the highest possible setting can be useful for other type of traps: if the emission time becomes very short the trapped electrons can be released within a packet transfer, resulting in no loss of charge. Table 1.1 lists relevant physical properties of the most common known traps induced by radiation. In the study of the radiation damage of the XRT I analysed the trap characteristics (such as their temperature and flux dependence and the trapped electrons release timescales) in individual pixels to derive estimates on the trap energy levels and the nature of the defects. As an example of the analysis, varying the flux of the X-ray illumination of the detector I investigated the sacrificial charge effect : at high X-ray fluxes, more than one charge packet is transferred over the damaged pixels during a frame readout; the first sacrificial package loses electrons to the traps, and if the re-emission timescale

52 23 Figure 1.11: Temperature dependence of the emission time of some of the most common types of radiation induced traps, given with their trap energy E t. Trap name Defect E t (ev) σx τ e (ms) E centre P-V Divacancy V-V Multivacancy V-V-V A centre O-V Table 1.1: Physical properties of common traps generated by irradiation, from Philbrick (2002) and Seabroke et al. (2008). The emission time constant is tabulated at T= 100 C. is longer than the transfer time the traps remain filled while the following X-ray packets are readout. Observing this effect at different operational temperatures poses stringent constraints on the emission times, providing insights into a trap characteristic energy and type. Radiation is not the only cause of charge traps in CCDs. Defects can also arise from detector design errors or manufacturing defects, such as an incorrect lattice structure alignment. These problems generally induce a potential barrier in the signal channel, and as a result charge is not properly transferred during a frame readout. Depending on the nature of the defect the amount of trapped charge can vary from 1-2 electrons per pixel to hundreds of electrons. In some cases the traps are uniformly distributed over the detector, and are called

53 24 spurious potential pockets ; in other cases the traps are only seen in some pixels, and are called localised traps. A localised trap can be caused for example by a pinhole in the gate dielectric (Janesick, 2001); during the doping process the dopant is deposited in the signal channel through the pinhole, causing a bump in the channel potential shape that can seize signal electrons. Impurities in the lattice structure can also become trapping sites, called bulk traps, typically capturing one or two electrons in the signal channel Charge Transfer Efficiency The effectiveness with which charge is transferred in a CCD is commonly described with a parameter called Charge Transfer Efficiency (CTE), defined as the ratio of charge transferred between two pixels. In state of the art, buried channel CCDs the process is very efficient, with CTE values between % and %. The complement of CTE is the Charge Transfer Inefficiency (CTI), defined as 1-CTE, it represents the fraction of charge lost during the transfer between two pixels. ThestandardandmostreliablemethodtomeasureCTEinCCDsiswithXray illumination, quantifying the charge lost between a number of pixel transfers N P. CTE = 1 (e ) X(e )N P (1.13) where X(e ) is the X-ray signal and (e ) is the amount of charge lost in N P transfers. CTE is measured for charge transferred through the CCD columns (parallel CTE, CTE P ) and the serial register (serial CTE, CTE S ), so that the total CTE is given by the combination of the vertical and serial charge losses ( (e ) = X(e ) (N PV CTI V + N PS CTI S )). CTE is dependent on several CCD operating parameters, as shown in the analytical formula derived in Kim (1979), CTE = 1 qn T V V e t PT 3τe [1 e ( t PT 3τe N Z t PT τe ) ] (1.14) where N T is the density of traps, V V is the volume of the charge packet, t PT is the transfer period, N Z is the average number of pixels between X- ray events and τ e is the charge emission time. CTE is therefore a function

54 25 of the source flux (through N Z ), the CCD temperature (through τ e, as lower temperatures increase the release time, and N T, as an increased dark signal at elevated temperatures can fill the traps) and the X-ray energy (through V V, as higher energy events generate a larger charge cloud). CTE is a global measure of the ability to transfer charge, as it provides the average value of the charge lost in the transfer process. In my study of the XRT CCD radiation damage instead, the analysis will be refined to measure charge losses (above the sensitivity threshold of 20 ev) and to derive the characteristics of the traps of individual pixels XRT radiation damage The changes in the gain and CTI of the XRT CCD caused by radiation have been monitored through the mission using the four 55 Fe radioactive sources on board the XRT that illuminate small areas at the corners of the CCD. The gain and CTI values are measured by fitting the 5.9 kev Mn-Kα emission line of the corner sources. The results of the analysis are illustrated in Figure 1.12, and indicate an increase in the gain coefficient and a worsening of the CTI due to the accumulation of charge traps caused by radiation. Observations of line-rich supernova remnants, as Cas A and Tycho have routinely been scheduled since the Swift launch for calibration purposes. The degradation of the spectral resolution of the CCD is clear in these datasets, as shown in Figure 1.13, which compares the Cas A X-ray spectrum as observed in 2005 and in The overall shift in energy scale to lower energies is the result of the average effect of charge traps, while the degradation of the energy resolution results from the spread in the amount of charge loss from trap sites at different locations on the CCD. InJune 2008, theregionofthedetector with datatelemetered totheground was increased to include the totality of the corner source events. This greatly increased the quantity and statistical quality of the data, allowing improved measurements of the time and temperature dependence of the gain and CTI coefficients. The richness of the data also permitted the study of the spectral response of individual pixels, by measuring the observed energy of the Mn-Kα emission line. The analysis revealed energy losses in some detector pixels of tens of ev or higher in the worst cases, implying the presence of tens of traps

55 Figure 1.12: Swift-XRT CCD gain and CTI measurements from the corner source data at a CCD temperature of -60 C from 2007-Sep-05 to 2012-Sept- 30. The top-left panel shows the measured 55 Fe K-α line energy fitted with a Gaussian function and the bottom-left panel shows the measured CTI values. The measurements are used to derive the gain and CTI coefficients (top right and bottom right panels) used in the XRT calibration gain file. Error bars are 1σ estimates. In the plot, the times are in MET, Mission Elapsed Time, the Swift spacecraft time, that uses as reference epoch the 1st of January, The Figure is from A. Beardmore, XRT calibration scientist. 26

56 27 normalized counts s 1 kev Cas A Cas A Energy (kev) Figure1.13: XRTWTmodeCasAspectrumin2005andin2010(Paganietal., 2011). The comparison shows an overall energy shift resulting from charge loss and the reduced energy resolution that causes the broadening of the brighter lines and the complete disappearance of the weaker ones. The silicon Kα line E = kev has a FWHM of 101± 3 ev and of 220± 12eV in the 2005 and 2010 datasets respectively, as measured in IDL using a modified Gaussian function(f e (x E) 2 2σ1 2 forx E,f e (x E)2 2σ2 2 forx<e)tomodeltheasymmetric distortion of the spectral lines caused by trap losses and a linear component to model the local continuum. generated by a radiation particle interaction with the silicon nuclei. These results are surprising, as the expected average number of displaced atoms by the interaction of protons with the silicon lattice is small, one or two, as discussed in Section 1.4. In light of these measurements, an extensive campaign was initiated to map charge losses in the imaging area of the detector using deep observations of the Cas A and Tycho remnants. The analysis confirmed the presence of badly damaged pixels that cause substantial energy losses during the charge readout process. 1.5 Thesis Outline Chapter 2 presents the observed effects of radiation on the Swift-XRT CCD. The damage is investigated through observations of the Cas A and Tycho supernova remnants and by the four 55 Fe radioactive sources on board the XRT.

57 28 Both datasets have revealed the existence of highly damaged pixels, in some cases presenting energy losses of more than 100 ev during the readout process, pointing to the presence of clusters of traps in single pixels. This result is at odds with the predicted damage from high energy protons, that are expected to cause displacement of only a few atoms in the silicon lattice, and could instead be explained by the production of secondary neutrons in the satellite body and the camera aluminium shield, whose nuclear interactions with the CCD silicon structure can cause the displacement of a large number of nuclei and generate multiple traps in a small volume. The analysis of the Cas A and Tycho datasets to map the pixels affected by radiation and to measure the energy losses is presented. This information is used by newly developed XRT software to correct the observed spectra for the effects of traps. The recovery of spectral resolution achieved thanks to trap corrections is shown along with examples of applications in scientific observations. In Chapter 3 the data from the 55 Fe corner sources are analysed to characterise the XRT radiation damage. Trap properties such as their energy level and the effects of the CCD temperatures on the charge losses are discussed. The corner source measurements are exploited in the effort to derive the ω parameter, the electron hole pair creation energy in silicon. This is made possible by the fact that a trap can capture a single electron, that in turn corresponds to and energy of ω ev. The measured energy losses in individual pixels caused by clusters of trapping sites are therefore multiples of the ω parameter when measured in ev. A feasibility study in the application of the novel method to derive the ionisation energy parameter using an XMM-Newton calibration dataset is also presented. In Chapter 4 I describe the laboratory program that I conducted at the Space Research Centre at the University of Leicester to study the radiation damage of an e2v CCD-22 similar to the CCD on board the XRT that was irradiated with a beam of 10 MeV protons by the XRT calibration team before the Swift launch. The dependence of the charge losses with respect to the operational CCD temperature, the X-ray energies and the flux was investigated; the results were used to introduce an empirical classification of the damaged pixels and are compared to what is measured for the traps on board the XRT. The measured dependencies are also used to constrain the energy levels of the traps and other physical parameters as the size of the trap clusters.

58 29 In Chapter 5 I investigate the scenario in which the large energy losses seen in the CCD on board the XRT are due to secondary neutrons. To this end, the proton damaged CCD-22 was also irradiated by an isotropic beam of 14 MeV neutrons at the Frascati Neutron Generator in October I first derive the expected flux of secondary neutrons produced by the interactions of protons in the Swift space environment with the CCD aluminium shield by using the Space Environment Information System (SPENVIS) Monte Carlo modelling package. I describe the detector neutron irradiation that I conducted at the Frascati generator and the datasets that I subsequently acquired at the Leicester camera test facility to investigate the properties of the neutron damage. While the overall CTE is not significantly degraded after the exposure to neutrons, implying a limited total number of nuclear interactions, large charge losses are seen in a few newly damaged pixels. The observed neutron trap properties are compared to the proton trap measurements and to the damage seen on board the XRT. In Chapter 6 I discuss and summarise the main results of my work, where the damage experienced by the Swift-XRT camera is compared to that seen after the proton and neutron irradiation of an e2v CCD-22 in the laboratory and put in contest with other current X-ray satellites. The comparison with XMM, in a highly eccentric orbit, is particularly interesting, as both X-ray telescopes are equipped with the same type of camera, the CCD-22. The XRT damage is also discussed in view of future planned satellite missions, as the results of the analysis of the XRT data and the laboratory experiments, that showed the impact on the CCD performance caused by proton and neutron irradiation, can influence design decisions such as the choice of material and the thickness of the camera shield and the optimal operational temperature of the detector; moreover, the occurrence of pixels with large energy losses due to radiation and their characteristics can provide useful information for missions such as Gaia where the modelling of the charge transfer efficiency is of critical importance to achieve the mission goals.

59 Chapter 2 Radiation damage of the Swift-XRT CCD and charge trap mapping 2.1 Introduction The spectral calibration of the XRT CCD before launch was performed at the University of Leicester Camera Test facility and at the PANTER X-ray test facility at the Max Planck Institute for extraterrestrial Physics (MPE) in September-October Data were collected using X-ray sources with line energies ranging from 0.28 kev (carbon Kα) to 8.05 kev (copper Kα) at a CCD temperature of 100 C and were used to model the spectral response of the camera (Mukerjee et al., 1988) and to derive the gain and the serial and parallel CTI coefficients (Morris, 2005). Using these datasets, a full width at half maximum (FWHM) of 135 ev was measured at 6.4 kev. Additional calibration measurements were carried out during Swift prelaunch tests at NASA s Goddard Space Flight Center. Data collected from four 55 Fe radioactive sources at the corners of the CCD and from a 55 Fe source mounted on the XRT camera door that illuminated the focal plane were analysed. The 55 Fe sources decay to 55 Mn through electron capture, emitting Auger electrons and the doublet X-ray line Kα1 and Kα2 at energies of and kev and Kβ X-rays of kev. The Mn Kα line from the door source was used to derive an average parallel CTI p value across the detector 30

60 31 Figure 2.1: Deep charge traps were found in pre-launch test data (from Morris (2005)). On the left panels, the energy of the events from the Mn Kα line (in DN units) are plotted along the columns; on the right panels, the median energy value of the Mn Kα events along the affected columns is shown. Deep traps are visible in columns 54, 78 and 110, while shallower energy losses are measured in pixels of columns 140, 259 and 294. of and a serial CTI s of when the CCD was operated at 100 C (Morris, 2005). The richness of these datasets also allowed the derivation of the parallel CTI of individual columns, whose measurement with a few exceptions was found to be consistent with the average CTI p value. The detailed investigation into the outliers revealed, for the first time, the presence of large charge traps in some columns of the XRT CCD. In particular, traps with losses of more than 100 ev were found in the corner source data in column 54 and in the door source data in columns 78 and 110, while shallower traps of the order of tens of ev were found in columns 140, 259 and 294 (Figure 2.1). During December of 2004, shortly after launch, 55 Fe door source datasets

61 32 were collected before the door was opened and the source moved out the field of view. The CCD temperature during these observations fluctuated between T= 65 C and T= 48 C, as by this time the power supply of the Thermo- Electric Cooler had failed and the camera was only cooled passively through the radiator. The investigation of the Mn Kα line energy confirmed the presence of the large trap losses in columns 54, 78 and 110, while shallower trap losses in columns 140, 259 and 294 were not detected, possibly because of the increased dark current filling the traps at the higher CCD operational temperatures (see Section 1.4.3). Moreover, new traps were noticed in columns 66, 324 and 376, while columns 173, 189, 248, 281, 306 and 351 presented a global shift of between 50 and 100 ev with respect to the expected line energy, caused by large charge traps in the CCD store section. In the store section there is no spatial information to identify the affected pixels, and the measured offsets with respect to the expected energy value are the result of the cumulative losses caused by traps in the store section columns. Examples are shown in Figure 2.2. The FWHM from the door source data was measured to be 150 ev at 5.89 kev, higher than the value measured pre-launch, mainly because of the effects on the gain and CTI coefficients induced by the varying CCD temperature during the acquisition of these datasets that were not fully modelled at the time. Early on in the Swift mission, because of telemetry constraints, the data volume generated by the XRT was reduced by only storing on board and later transmitting to the ground the photons detected in the central 500x500 pixels oftheccd.becauseofthis, averylimitednumber ofcorner sourcex-rayswere available for analysis and the CTI evolution could not be reliably monitored. Ad-hoc tests with a larger 600x600 pixels window were therefore performed in August of 2005 and CTI values of CTI s = and CTI p = were derived using the full corner source data. These measurements indicated a degradation caused by radiation twice as fast as the one experienced by the MOS cameras on board XMM-Newton, initially operated at 100 C. In June 2008, to better monitor the evolution of the spectral response, the XRT flight software was updated to operate the CCD permanently in full frame PC mode, with the entire 600x600 imaging area telemetered to the ground. This allowed the precise measurement of the CTI as a function of time and temperature (Figure 1.12) and evidenced the presence of numerous pixels with new traps causing large charge losses in the areas of the detector illuminated by the

62 Figure 2.2: 55 Fe door source datasets collected shortly after Swift launch. Top panels - Traps with large trap losses in columns 66 and 324; Bottom panels - Columns 281 and 351 present overall energy offsets caused by traps in the CCD frame store section. 33

63 34 Figure 2.3: Trap mapping observations. (Left panel) - February 2009 Cas A campaign - Mosaic of the Cas A offset pointings displayed in detector coordinates (DETX are the columns and DETY the rows coordinates). The remnant covers approximately an area of 100 pixels radius of the detector in a single pointing. (Right panel) - Tycho trap mapping observations in detector coordinates; the set of offset pointings are aimed at obtaining the largest statistics in the central region of the CCD, where the majority of science targets are imaged in pointed observations. The images are scaled differently for Cas A and Tycho observations to evidence the location of the various offset pointings. radioactive sources. To investigate the damage within the XRT FoV a calibration campaign consisting of several Cas A supernova remnant observations was carried out in February of A total of eight offset pointings were performed to cover a largeareaofthedetector (Figure2.3, leftpanel), withanexposure timeof15ks per pointing to collect a sufficient number of X-rays from the SNR silicon Kα line to map the damaged pixels. The silicon Kα is the strongest emission line of the remnant, with an average energy of kev, but presenting variations of up to 20 ev due to Doppler velocities that can reach up to ± 1000 km s 1 across the remnant (Willingale et al., 2002). Using the value of kev as the reference line energy, the observations evidenced the presence of tens of pixels in the imaged area of the detector with energy losses of 20 ev or greater.

64 Trap mapping The test carried out in February 2009 using Cas A observations proved that charge traps could be successfully identified and localised and that energy losses larger than 20 ev could be measured in individual pixels. A trap mapping program was therefore implemented, with the final goal of restoring the spectral resolution of the CCD to near that at launch. The Tycho supernova remnant was preferred over Cas A because in spite of being fainter, thanks to its larger size it requires a shorter total exposure time to collect a sufficient number of X-rays from the silicon Kα line. Additionally, in Tycho the silicon line presents a greater uniformity across the remnant (the Doppler shift is smaller than in Cas A), resulting in a more accurate measurement of the charge losses due to traps; the average value of the line energy across the remnant is kev, as derived from the analysis of XMM spectra, and is used as the reference energy in the trap mapping analysis. In the following sections, I describe the strategy chosen to optimise the Tycho observations to monitor effectively the evolution of the radiation damage; the techniques developed to map the damaged pixels and to measure the energy losses in the two XRT operational modes are presented. I describe the updates in the gain files format to include measurements of the energy losses, and how the Swift-XRT software was modified to apply the energy corrections to the X-ray events affected by traps Photon Counting mode The imaging strategy adopted for the trap mapping Tycho observations is displayed in Figure 2.3, right panel, and was adopted to optimise the scientific return of the investment in calibration time (the allocated budget for calibrations for the Swift mission is 10% of the total observing time). This configuration, in fact, achieves the highest silicon Kα line statistics in the central 200x200 window of the CCD, where, thanks to the good Swift pointing accuracy ( 3 ), virtually every GRB afterglow and most other X-ray science targets are imaged. Five pointings, with an exposure time of 15 ks each, are closely spaced along the central columns of the CCD, with the remnant centred at detector coordinates (DETX,DETY) = (300,250), (300,275), (300,300), (300,325) and

65 36 (300,350). In spite of the remnant being brighter in X-rays at the rims than at its centre, thanks to this exposure mosaic a good level of uniformity is achieved in the central CCD region, with an average of twelve silicon line photons detected per pixel. Two additional 15 ks pointings are scheduled on the left and right areas of the detector; these observations allow the derivation of the energy offset of individual columns, caused by charge traps in the CCD store section and the serial readout register, and to identify and measure the depth of the worst traps ( 50 ev) in the left and right areas of the detector. The following procedure is followed in the trap mapping analysis (with the term deep trap I refer to the ensemble of traps, or trap cluster, that causes energy losses of 20 ev in an individual pixel during the CCD readout transfer of a silicon Kα photon) and will be described in detail below: Derivation of the gain and the parallel CTI coefficients representative of the columns least affected by the presence of deep traps; Processing of Tycho observations using updated gain and parallel CTI coefficients; Measurement of the silicon Kα line energy along CCD columns; Identification of columns with deep traps and refinement of their pixel positions; Measurement of the silicon Kα line energy in segments of columns without deep traps and calculation of the offsets from the reference energy value; Generation of a new gain file storing information on the trap positions and the energy offsets; Reprocessing of the Tycho data with the new gain file; Verification of the correct application of the energy offsets During the first few years of the mission the gain and CTI coefficients were measured using every Mn Kα line event collected from the corner sources. With the beginning of the trap mapping initiative a different set of gain and CTI coefficients has been derived to better reflect the response of the detector after

66 37 Figure 2.4: The gain coefficient at T= 60 C, estimated from the five columns with the highest Mn Kα line energy in CS3, is used to process trap-corrected spectra (in green the measured monthly averaged gain values, in red the modelled gain evolution with time). Error bars are 1σ estimates. trap losses corrections. The change consisted in calculating the gain coefficient using only the columns in the bottom-left corner source (CS3, the one closer to the output amplifier) with a centroid energy within 50 ev of the highest measured value. The coefficient is derived monthly to guarantee the necessary statistical quality of the data, and dividing the data in 1-degree temperature bins to model the gain temperature dependence. The temporal evolution of the gain coefficient so derived at 60 C is shown in Figure 2.4. In the same fashion, as trap mapping allows the correction of charge losses in pixels with deep traps, the residual parallel CTI is better described by the columns least affected by traps. Columns with a difference in the Mn Kα line energy between the top and bottom corner sources of less than 25 ev are selected to derive the parallel CTI, merging data collected monthly and using 1 C temperature bins to model the temperature dependence. The serial CTI is instead set to zero, as the energy offsets of each columns measured using Tycho observations effectively reproduce its effects. The gain and CTI coefficients so derived are included in an updated version of the gain file used to reprocess all the Tycho offsets pointings. Using the reprocessed Tycho data, the energy of the Si-Kα line is derived merging events from 20 adjacent pixels incrementally along each detector column, that is, measurements are derived for the column segments DETY =

67 38 Figure 2.5: Profile of the Si-Kα line energy in column 256 from Tycho trap mapping observations. These plots are used to identify columns affected by deep traps, as the one present in row DETY=310. [ ], [ ], [ ] and so on. The energy is obtained by fitting the line with a Gaussian plus a constant model, using the IDL MPFIT package developed by Craig Markwardt and available online from the IDL repository 1, and Cash statistics (Cash, 1979) to properly account for the low number of X-ray events. This process determines the columns affected by deep traps and provides an initial estimate of the trap position by identifying large differences in energy in separate segments of columns (Figure 2.5). The position of the traps are later refined by fitting the emission line from events detected above and below the initially estimated trap location. For the deepest traps, presenting energy losses 50 ev, the exact location of the damaged pixel can be determined with this method, while for shallower traps the combination of low photon statistics, the intrinsic spectral response resolution and the stochastic nature of the trap capture and emission processes, the position can be pinpointed with an accuracy of approximately 5 pixels. A modified gain file format has been developed to allow the recording of the trap information derived from the mapping analysis, consisting of trap locations, energy losses and the extent of the column segments affected by the traps. The information is used by the new version of the task xrtcalcpi, part of the XRT pipeline software, to correct the measured event energies for the losses due to traps. The implemented corrections are tested by reprocessing the Tycho dataset running xrtcalcpi with the new gain file as input. Each CCD column is inspected to verify that the Si-Kα is measured at the expected 1 Markwardt IDL repository, html

68 39 Figure 2.6: The Si-Kα line energy in column 256 after trap corrections are applied processing the Tycho calibration observations using the updated gain file. The energy of the line is restored to the expected value after trap corrections have been applied. energy after the offsets have been applied; as example, the result for column 256 is shown in Figure 2.6. The corrections are additionally tested from short (5-10 ks) Cas A observations taken within weeks of the Tycho calibration datasets and processed with the new gain file. The comparison of the Cas A trapcorrected spectrum and the observed, trap-affected spectrum shows a recovery in resolution with better defined lines at the expected energies Windowed Timing mode WindowedTimingmodehasatimingresolutionof1.8msthatisachievedatthe expense of spatial information: only the column DETX coordinate of the X-ray events is retained, while the exact row DETY coordinate is lost. Nevertheless, if a source sky coordinate (RA, Dec) is provided when processing the data with the XRT pipeline, the average row location of the events is recovered. It was therefore at first thought that the information on the trap locations derived from PC mode observations could also be used for the correction of WT data. WT mode observations of the Tycho remnant positioned just above and below known traps showed instead that the charge losses in WT mode could not be reliably estimated based on PC mode measurements. This was confirmed by the investigation of Tycho observations in PC and WT mode taken at similar epochs, that evidenced a large scatter in the charge losses affecting the two modes. A direct comparison of the losses can be made if the remnant is imaged over the same part of the detector in both modes. This is the case for example of the PC and WT mode Tycho observations taken in August of Figure 2.7

69 40 shows the energy offsets in individual CCD columns derived from the fits of the reference Si-Kα line (E ref = kev) with a Gaussian plus a constant model in the two modes. A linear correlation between the WT and PC mode charge losses well describes the data (with index α = 0.95±0.06 and intercept β = 141±8 for this epoch), but a large scatter, of the order of 100 ev, is seen in the measurements. The scatter can be attributed to the presence of multiple type of traps with specific charge capture and release times (Equations 1.11 and 1.12) with different effects in PC and WT data due to the distinct readout process and timescales in the two modes. Generally, WT mode charge losses are larger, and even columns with little or no losses in PC data are instead affected by large energy shifts in WT observations. This can be explained by the build-up of thermal background noise during the 2.5 seconds long PC frames exposure that can partially fill charge traps in the store section and the serial register during the frame readout process, while in WT mode instead the CCD is continuously clocked out and the thermal noise is lower. Because of this intrinsic limitation, a different approach has been taken to estimate the energy lost to traps in WT mode observations. Three observations of the Tycho remnant, with an exposure of 15 ks each, are scheduled at detector coordinates DETX, DETY = (300,100), (300,300) and (300,500). As the remnant has a radius of approximately 100 pixels, these offset pointings cover the bottom, centre and top sections of the WT readout window, that consists of the central 200 columns of the CCD. The energy of the Si-Kα line is measured by fitting the line with a Gaussian plus a constant model. Its offset from the reference energy E ref = kev provides the average charge losses due to the cumulative effect of the traps in the bottom, centre and top segments of each column. The energy offsets are included in the new version of the WT gain file to apply the trap corrections to the measured energy of the detected X-ray. As in the PC mode case, WT mode Cas A observations taken close in time to the Tycho pointings are used to verify the validity of the corrections included in the new gain file.

70 Figure 2.7: Charge losses measured from PC and WT mode observations of the Tycho SNR in August of 2013, using the Si-Kα line reference energy E ref = kev. A linear correlation with index α = 0.95±0.06 and intercept β = 141±8 well describes the data but a large scatter in the datapoints prevents the use of energy losses measured in PC mode to estimate and correct for the energy offsets in WT observations. 41

71 Energy dependence CTI measurements from previous X-ray missions (e.g. Chandra and Suzaku) and laboratory experiments(prigozhin et al., 2004) have shown that the amount of charge lost to traps depends on the incident photon energy. CTI models attribute the observed behaviour on the size of the charge cloud generated by the absorption of an X-ray photon: during the readout process the charge packet is confined under the potential well of the electrodes, and it interacts with a number of traps that is a function of the charge cloud volume. In the case of small charge packets the volume of the cloud can be instead considered constant (Holland, 1993); in this case the observed dependence is interpreted in terms of a density model, where higher density signals lose more charge during the transfer process. In the case of the XRT, the energy dependence of the charge losses is modelled with a broken power law to account for the possibility of a transition from a density driven to a volume driven regime above a break energy E break. ( E (E) = (E break ) ( E = (E break ) E break E break ) α1 (E E break) ) α2 (E > E break) (2.1) The Si-Kα is the only emission line in Tycho with enough statistics in XRT observations to measure its variations across the detector. The dependence can not therefore be modelled for individual traps, but only globally. The sulphur Kα and iron Kα lines of Tycho and Cas A (E S,Tycho = kev, E Fe,Tycho = kev and E S,CasA = kev, E Fe,CasA = kev) are used to model the dependence at high energies, above E break. In PC mode, the instrumental nickel Kα line at E Ni = kev is also used, merging data from all PC observations over a yearly period to obtain sufficient statistics in the line. The line can not be used to model the energy dependence in WT data as it lacks the sufficient statistics. This is due to the fact that WT mode observations are less frequent than PC ones, have a smaller readout window and the signal from the observed science target is generally quite bright. At lower energies, calibration observations of the SNR E are scheduled twice a year in

72 43 PCandWTmodetoderive theenergydependence usingtheoxygen(0.570kev and kev) and neon (0.910 kev and kev) emission lines as reference energies (Plucinsky et al., 1988). The spatial extension of the E0102 remnant is limited (approximately 30 pixels radius) and the lines are not bright enough to allow the modelling of the dependence of individual pixels, therefore only an average dependence is derived. From the fits of the lines in XSPEC, α 1 = 0.85 and α 2 = 0.80 were derived for PC mode observations, with a break at the reference energy E break of kev, and α 1 = 0.8 and α 2 = 0.50 for WT mode, with E break of 3.0 kev. Two examples of the results of trap corrections, applied using the derived energy dependence are presented in Figure 2.8. On the left panel, the nickel line extracted with and without trap corrections is shown. On the right panel, the improvement in spectral resolution obtained in E0102 observations after deriving the trap losses energy dependence below E break can be seen. A particularly important dataset consists of the E0102 observations in PC mode obtained in September In that case the remnant was repeatedly imaged over the same area of the detector, accumulating a sufficient number of X-rays from the source to allow the fit of the brighter oxygen line at kev in individual detector columns. The energy offsets measured from E0102 data were compared to the ones derived from the Si-Kα line in Cas A observations of that epoch. The comparison showed that the implementation of the derived average energy dependence provided the correct scaling of the trap losses at lower energies in individual columns. 2.4 Temperature dependence The XRT CCD temperature during an observation varies depending on the efficiency of the radiator passive cooling. It can range from 75 C in the coolest periods up to 45 C in the configuration with the radiator facing the Earth, with the large majority(90%) of the time spent at temperatures between 65 Cand 55 C. TheCCDtemperatureasafunctionofthespacecraft orbit and orientation has been modelled and can be controlled to a certain extent (Kennea et al., 2005). The Swift science planners avoid scheduling observations at the warmest temperatures when the XRT thermal background noise raises

73 44 Figure 2.8: Energy dependence of the charge losses. (Left panel) - the instrumental nickel Kα line is used to derive the energy dependence of the charge losses above E break ; the observed line resolution and flux (in black) improves after trap corrections are applied (in red). (Right panel) - SNR E is used to derive the energy dependence below E break ; the comparison of the observed spectrum (in black) and the trap-corrected spectrum (in red) evidences the improved spectral resolution after the appropriate energy dependence has been derived. rapidly and the presence of numerous hot pixels compromises the quality of the data; high CCD temperatures are nevertheless beneficial in terms of charge losses, as the thermally generated background electrons partially fill the charge traps. At the other extreme, when the CCD is colder, the traps emission time becomes so long that once a trap is filled by charge the captured electrons are not released until a much later time: the trap is said to be frozen, and does not affect the charge of the following packets; for this reason, for example, the temperature of the MOS CCDs on board XMM-Newton was lowered from 100 C to 120 C two years into the mission, improving the charge transfer efficiency. The effects of temperature on the trap losses are seen in PC mode in Tycho observations and from the analysis of the instrumental nickel line (Figure 2.9, top panels). The Si-Kα line in Tycho data and the Ni-Kα line are measured at higher than expected energies at high temperatures, when the dark current partially fills the traps; the temperature dependence of the line energies appears linear between 65 C and 55 C and it flattens at the lowest temperatures, where the thermal noise is stable and the trap emission times get longer. The effect is not seen in WT mode data, where the Si-Kα line in Tycho is measured

74 45 Figure 2.9: The temperature dependence of the trap losses causes variations in the measured energy of the Si-Kα in Tycho PC observations and in the Ni-Kα background instrumental line (top panels). The implementation of the observed dependence in the PC gain file brings the energy of the lines close to the expected value at all temperatures (bottom panels, data from Tycho August 2013 and nickel 2013 PC datasets). at the same energy at all temperatures; this is likely due to the faster and continuous readout process that prevents the filling of traps by the background charge generated during the exposure of the PC frames. In the PC gain files the observed temperature dependence is taken into account by introducing energy offsets at T= 75 C, 61.5 C and 48 C. When the data are processed, the XRT software applies an energy correction at the operational CCD temperature by linearly interpolating between the three offsets. The results of the correction can be seen in the bottom panels of Figure 2.9, with the Si-Kα line in Tycho and the background Ni-Kα line at the expected energies over the entire temperature range.

75 46 + PC Cas A PC Cas A PC Cas A PC Cas A 2013 normalized counts s 1 kev Energy (kev) normalized counts s 1 kev Energy (kev) normalized counts s 1 kev WT Cas A WT Cas A Energy (kev) normalized counts s 1 kev WT Cas A WT Cas A Energy (kev) Figure 2.10: The effects of radiation damage as seen in Cas A observations taken in early 2005 and in late The top panels compare the spectra extracted from PC data, in the 1-8 kev energy range (left panel) and around the Fe-Kα emission line (right panel). The bottom panels compare spectra from WT data focusing on the energy range of the stronger emission lines (left panel) and the Fe-Kα emission line (right panel). 2.5 Recovered energy resolution The severe loss in spectral resolution in PC and WT mode data can be seen in Figure 2.10, that compares observations of the Cas A SNR taken in early 2005 and at the end of The continuous exposure to radiation in space for almost 9 years has caused the broadening of the brightest emission lines of the remnant and the complete disappearance of the weaker ones. In these PC observations, the Si-Kα line at the energy of kev has a FWHM of 109±3 ev in 2005 and of 193±12 ev in 2013; in WT mode, the FWHM is 101±3 ev and 240±12 ev respectively. The use of the updated version of the XRT pipeline software and the new gain files corrects the XRT data for the charge lost to traps. The gain files for both PC and WT mode observations consist of monthly entries storing values (atthereferenceccd temperatures of 48.0 C, 61.5 Cand 75.0 C)ofthe

76 47 gain coefficients (GC0), the serial and parallel CTI coefficients (GC1 and GC2 respectively), the energy scale offsets (GC3) and the position-dependent energy offsets (OFFSET) and trap locations(rawx, RAWY, YEXTENT). The serial CTI coefficients (GC1) are set to zero as the charge losses in the serial direction are taken into account by the position-dependent energy offsets. Additionally, the gain files include entries for the energy dependence of the CTI (BETA1, BETA2, E CTI) and the energy dependence of the trap offsets (ALPHA1, ALPHA2, EBREAK), modelled with broken power law functions. The energy of the X-ray events is measured in a 12 bit digital number (DN). The conversion to evs is obtained using the following expression: E(eV) = DN (GC0+X GC1+Y GC2)+GC3 (2.2) where X and Y are the detector coordinates of the X-ray event. When data are processed using the XRT pipeline, the measured DN energy of the X-ray events is firstly multiplied by the gain coefficient (GC0); the corrections for the residual charge losses not taken into account by the trap mapping analysis are then calculated using the parallel CTI coefficient (GC2) and by applying the energy scale offset (GC3). The gain and CTI coefficients are selected by the software by matching the time of the gain file entries with the X-ray event detection times and by linearly interpolating between the CCD reference temperatures. A detailed description of the coefficients, their derivation and measurements can be found in the Swift-XRT CALDB Gain Release note (Pagani & Beardmore, 2013). The position-dependent trap energy corrections are subsequently performed by the ftool xrtcalcpi, which uses the new CALDB gain file, included in the XRTDAS software release (starting from version 2.7.0). The appropriate energy offset is applied based on the X-ray event arrival time and the CCD temperature. An iterative approach has been implemented in xrtcalcpi to derive the energy correction; the first iteration estimates the intrinsic event energy, which is used to quantify the trap charge losses. This iterative process is repeated twice and it assures that the correction is evaluated based on the intrinsic rather than the measured photon energy, as is required by the energy dependence of the charge losses. The result of this process is a substantial recovery in the spectral resolution.

77 48 The first trap mapping campaign was conducted in October 2007 using Cas A observations; the improvement obtained from WT spectra is shown in the left panel of Figure The Si-Kα line in the corrected spectrum is narrower and has a higher peak, and the weaker lines are enhanced thanks to the correctly described charge loss. Tycho was first used for trap mapping in October 2009; the results of the trap charge corrections is illustrated in the right panel of Figure normalized counts s 1 kev Observed + Corrected Energy (kev) normalized counts s 1 kev Observed + Corrected Energy (kev) Figure 2.11: Recovery of spectral energy resolution. (Left panel) - Comparison of the observed and the trap corrected spectra extracted from WT observations of the Cas A SNR in October The fit of the Si-Kα line with an asymmetric Gaussian in IDL and a linear component to model the local continuum yielded FWHM = 159± 13 ev for the observed 2007 spectrum and 106± 3 ev for the corrected 2007 spectrum. For comparison, the FWHM value during an observation in February 2005 (shortly after launch) was 101 ± 3 ev; (Right panel) - Results of trap mapping and corrections from PC mode observations of the Tycho SNR taken in October As radiation continues to damage the CCD the spectral resolution worsens, with a FWHM of the Si line of 132± 3 ev after trap corrections in this case. As the radiation damage accumulates over the years, the recovered spectral resolution also declines. More pixels are affected by the presence of traps and larger charge losses occur during the readout process. Because of the stochastic nature of the trap charge capture and release process, the trap loss measurements and corrections become less accurate, and only partially successful in recovering the intrinsic spectrum. This is clearly illustrated in Figure 2.12, comparing Cas A spectra as measured shortly after launch, and the measured and trap-corrected spectra in 2013 observations; spectra from PC and WT mode observations are shown in the left and right panels respectively. The evo-

78 49 normalized counts s 1 kev PC Cas A 2005 Observed + PC Cas A 2013 Observed + PC Cas A 2013 Corrected normalized counts s 1 kev WT Cas A 2005 Observed + WT Cas A 2013 Observed + WT Cas A 2013 Corrected Energy (kev) Energy (kev) Figure 2.12: Loss in spectral resolution and recovered fraction thanks to trap energy corrections between Cas A observations taken in early 2005 and in late (Left panel) - PC mode observations; (Right panel) - WT mode observations. lution of the resolution of the detector is reported in Table 2.1 which compares the FWHM of observed and corrected spectral lines (silicon, sulphur and iron Kα) in observations of Cas A and Tycho utilised for trap mapping at different epochs. The Cas A lines are at energies E Si = kev, E S = kev, E Fe = kev, while for Tycho E Si = kev, E S = kev, E Fe = kev, as derived from our fits to XMM spectra; the differences in the line energies originate from the dynamics of the expanding SNR shells. Figure 2.13 illustrates the evolution of the FWHM of the Si-Kα line in these remnants since the beginning of the trap mapping program in Trap corrections almost completely recovered the resolution at launch in spectra obtained in 2007, while the damage in later observations is too severe to allow a full recovery of the X-ray energies. 2.6 Trap mapping limitations The corrections derived from the trap mapping analysis do not fully recover the spectral resolution of the detector at launch. The limitations still present are both intrinsic to the employed mapping technique and also related to the nature of the charge trapping and release processes. A few issues have already been discussed: the low statistics of the reference emission line and its nonuniformity across the Tycho remnant limit the precision to which the pixels with traps can be pinpointed and their losses measured: only pixels presenting

79 50 Table 2.1: XRT instrumental full width half maximum (FWHM) in the observed and the corrected spectra of each calibration epoch (specified as YYYY/MM). The total exposure time of the observations used for the trap analysis for each epoch is reported in kiloseconds (ks). The FWHM values at sulphur andironareonlyreportedwhenenoughcountsinthelinesallowedareliable fit. The last column quantifies the improvement in the energy resolution R defined as R = (R O R C ) R O, where R = FWHM E. Source Mode Epoch Exposure Line FWHM observed FWHM corrected R CasA PC 2005/ Si 108 ± 4 S 133± 6 Fe 268± 8 CasA PC 2007/ Si 138 ± ± ± 0.05 S 200± ± ± 0.06 Fe 318± ± ± 0.09 CasA PC 2009/ Si 154 ± ± ± 0.05 S 251± ± ± 0.08 Fe 372± ± ± 0.08 Tycho PC 2009/ Si 179 ± ± ± 0.05 S 267± ± ± 0.06 Fe 381± ± ± 0.14 Tycho PC 2010/ Si 177 ± ± ± 0.04 S 256± ± ± 0.05 Fe 381± ± ± 0.13 Tycho PC 2010/ Si 192 ± ± ± 0.05 S 269± ± ± 0.06 Fe 387± ± ± 0.11 Tycho PC 2011/ Si 185 ± ± ± 0.05 Tycho PC 2011/ Si 191 ± ± ± 0.05 Tycho PC 2012/ Si 191 ± ± ± 0.05 Tycho PC 2012/ Si 198 ± ± ± 0.05 Tycho PC 2013/ Si 206 ± ± ± 0.05 CasA WT 2005/ Si 101 ± 3 S 128± 6 Fe 263± 8 CasA WT 2007/ Si 159 ± ± ± 0.09 S 244± ± ± 0.07 Fe 383± ± ± 0.06 CasA WT 2008/ Si 161 ± ± ± 0.09 S 274± ± ± 0.08 Fe 393± ± ± 0.07 CasA WT 2009/ Si 177 ± ± ± 0.10 Tycho WT 2009/ Si 196 ± ± ± 0.09 Tycho WT 2010/ Si 219 ± ± ± 0.06 Tycho WT 2011/ Si 234 ± ± ± 0.06 Tycho WT 2011/ Si 234 ± ± ± 0.06 Tycho WT 2012/ Si 243 ± ± ± 0.06 Tycho WT 2012/ Si 249 ± ± ± 0.06 Tycho WT 2013/ Si 270 ± ± ± 0.05

80 Figure 2.13: Evolution of the FWHM of the Si-Kα line of the Cas A (in 2007 and 2008) and of the Tycho (since 2009) SNRs, in observed and trap-corrected spectra for PC and WT mode data. At the beginning of the mission, in February2005, thesi-kα lineofcasaspectrahadameasured FWHM PC = 108± 4 and FWHM WT = 101± 3. 51

81 52 energy offsets greater than 20 ev can be identified, while shallower traps are treated as part of the overall CTI coefficients. In WT mode observations, cumulative energy offsets in three sections of each column are applied to correct the measured energies, while charge losses of individual pixels cannot be measured and corrected for. The modelling of the energy and temperature dependence of the trap losses is applied globally on every trap. While the modelling improves the quality of the data (see for example the successful recovery of the instrumental nickel line in Figure 2.9) it is known and it will be discussed in more detail later (Sections and 4.6), from both XRT corner source data and from the analysisofaprotondamagedccd-22,thesametypeofdetectoronboardswift, that traps in individual pixels have different behaviours with respect to CCD temperature and photon energy due to their characteristic capture and emission times, theirlocationinthepixel, thedensityofthetrapsandthesizeofthetrap cluster. An additional important factor that can influence trap measurements is the source brightness: traps can be filled during a CCD frame readout by the passage of a preceding charge packets, so appearing shallower to subsequent X- ray events. This sacrificial charge effect has been investigated and observed in some traps of the proton-damaged CCD (see Section 4.8), but there are no bright calibration sources to measure and model it on board Swift. The effect could become particularly important in WT mode observations, that are typically used for high flux sources. Lastly, a small fraction of XRT observations are affected by bright Earth contamination, optical light from the lit-side of the Earth; during the times of contamination, the charge generated by the optical photons can fill the traps reducing the effective trap depth and distorting the spectra. Most of the complications presented above could in principle be dealt with by increasing the photon statistics and by implementing software changes to model the energy and the temperature dependencies derived for each pixel with traps. The total exposure time allocated to the Tycho calibration observations is however limited by the optimal trade-off between the best achievable spectral quality of XRT data and the overall scientific return of the Swift mission. The stochastic nature of the electron capture and release processes by the traps poses instead a physically intrinsic limit that can not be overcome; as discussed previously, a charge trap captures and releases electrons with proba-

82 53 normalized counts s 1 kev Observed + Corrected Energy (kev) Figure 2.14: Trap correction application. The 5.5 ks observation of the Cas A SNR taken in August 2010 in PC mode demonstrates the validity of trap corrections when applied on datasets other than those used to define the trap mapping calibrations. The Si-Kα line has FWHM of 167 ± 10 ev in the observed spectrum and of 135 ± 7 ev after trap corrections derived from Tycho calibration observations from March 2010 are applied. bilities defined in Equations 1.11 and Better statistics of the Si-Kα line can improve the precision in the measurement of the average charge losses but the deviations from the mean will increase as the build-up of damage in the course of the mission generates more and more traps. The accuracy of the XRT energy scale of corrected spectra can be estimated by using short on-axis observations of the Cas A SNR taken months apart from the trap mapping calibration epochs. In a fit of the trap-corrected spectrum of the remnant with a model derived from XMM-Newton observations, differences in energy of the E Fe line of the order of 20 ev from the XMM values are measured in the PC spectrum, while in WT mode the differences can be higher, up to 30 ev. The short Cas A datasets also demonstrate the validity of trap corrections for observations not used in trap mapping calibrations. As an example, Figure 2.14 shows the recovery in energy resolution for a 5.5 ks PC mode observation of Cas A taken in August 2010, corrected with trap measurements derived using Tycho calibrations of March The FWHM at kev improves from 167±10eV to 135±7eV after trap corrections are applied, consistent with the results on Table 2.1.

83 54 Figure 2.15: Swift triggered on a flare of the EV Lac star in April of 2008, promptly slewing to observe the source with the narrow field instruments. The XRT lightcurve (left panel) consists of WT data during the first orbit of observations, followed by PC data in subsequent orbits as the source had faded. A fluorescent iron line at 6.4 kev was reported in the analysis by Osten et al. (2010), who processed the data with the XRT software and gain files available at that time, that did not include trap corrections (right panel). 2.7 Trap mapping applications The improvement in spectral resolution and accuracy of the energy scale obtained through charge trap corrections can have a significant impact in the analysis of XRT data, better constraining physical parameters derived from the spectral modelling. In extreme cases the distortion caused by charge losses can even lead to an incorrect interpretation of the physical phenomena, a risk avoided by correcting the data for the effects of traps. A first example of the impact of trap corrections is GRB , a very bright burst at a redshift of 0.54, with an associated supernova identified from optical photometry. Page et al. (2010) processed the GRB dataset using the newly developed trap-corrected gain file, and reported the detection of a thermal component during the early X-ray afterglow decay in addition to the non-thermal synchrotron emission. Our re-analysis of this dataset using the older, non-trap corrected calibration files showed that, although the power law indices were not significantly steeper, the blackbody temperature was significantly lower (e.g., kt= kev, compared to kev at s after the BAT trigger) than when analysed using the new trap-corrected files. A second interesting case is the dataset of the flare star EV Lac that trig-

84 55 normalized counts s 1 kev Observed + Corrected Energy (kev) normalized counts s 1 kev All columns + Most damaged Energy (kev) Figure 2.16: The effect of charge traps in the spectrum of EV Lac. (Left panel) -The April 2008WT observed spectrum (inblack) of theflarestar EV Lacafter the source has faded to a count rate below 150 cts s 1 is compared to the trapcorrected spectrum (in red). The corrections enhance the main spectral peak at 6.7 kev, but no improvement is seen in the definition of the proposed emission feature at 6.4 kev. (Right panel) - Using the previous, non trap-corrected gain file, the WT observed spectrum of EV Lac during the late decay of the flare (in black) is compared to the spectrum extracted from the columns most affected by radiation damage (in red). Charge traps cause a shift of the X-ray events to lower energies, that in the most damaged columns results in a bump at energies between 6.3 kev and 6.6 kev. gered Swift in During the first orbit of observations the X-ray emission was bright and the flare was observed in WT mode, while in later snapshots the source had faded and PC mode was used (Figure 2.15, left panel). Osten et al. (2010) modelled the WT mode spectrum at different time intervals with a two-temperature APEC model (Smith et al., 2001) and reported the additional detection of a fluorescent iron line at 6.4 kev during some intervals of the initial flare decay (Figure 2.15, right panel). In their analysis, the data was processed using the latest version of the XRT software and the WT gain files available in the CALDB at the time, that did not yet include trap corrections. To clarify the origin of the proposed fluorescence line I reprocessed the EV Lac data using the new gain file that included trap corrections. A comparison of the spectra extracted with the new and the old gain files, after the source has faded to a count rate below 150 cts s 1 and pile-up becomes negligible, is shown in the left panel of Figure 2.16; the main Fe line at 6.7 kev in the APEC model is better defined in the trap-corrected spectrum, while no recovery of the proposed fluorescence line is achieved, and instead it becomes insignificant.

85 56 To investigate this result we exploited the information derived from the trap analysis in WT mode to identify which detector columns are most affected by radiation damage i.e., the columns that present the largest offsets in the measured event energy. We then extracted observed source spectra from the most damaged columns separately from the remainder of the CCD columns. The spectral comparison shown in the right hand side of Figure 2.16, using the older non trap-corrected gain, suggests that the feature at 6.4 kev is partly due to the energy offsets introduced by these badly damaged columns. Trap losses influence the detection significance and inferred variability of the fluorescent Fe line from EV Lac, suggesting that this aspect should be reassessed using the latest version of the gain files. 2.8 Summary The critical aspect of the study of the radiation damage experienced by the XRT in the space environment is the discovery of damaged pixels with large energy losses. This result is at odds with what expected from proton irradiation, whose interaction in silicon is predicted to generate single electron traps, and is generally used to estimate the radiation hardness and the degradation of the detectors to be used in satellite missions. The presence of damaged pixels with large charge losses could compromise the quality of data of projects like ESA s Gaia mission that aims at measuring position and velocities of a billion stars with unprecedented accuracy. I described the Tycho and Cas A calibration observations used to map the damage and to measure the charge losses in individual pixels and showed that the energy corrections implemented in dedicated software successfully recover the spectral resolution of the detector to values near those at launch. The trap mapping observations also revealed the existence of temperature and energy effects on the measured charge losses. These dependencies are modelled and corrected for via both calibration observations and the instrumental nickel line. Examples were also presented of the significant impact of trap corrections on the scientific quality of XRT datasets. The trap corrections provided more accurate estimates of the physical parameters of the observed X-ray sources through their spectral modelling.

86 Chapter 3 The XRT calibration corner sources and a novel method to derive the electron-hole pair creation energy of silicon 3.1 Introduction The X-ray events collected from the 55 Fe calibration sources are a rich mine to investigate the effects of radiation as experienced by the XRT in orbit. When in June 2008 the XRT flight software was updated to operate the CCD permanently in full frame PC mode, the entire 600x600 imaging area started to be telemetered to the ground, and all the corner source data became available for the radiation damage analysis. In 2008, approximately 250 events per pixel were collected from the 55 Fe sources in a month; with a half-life of years, their intensity has dropped considerably, as illustrated in Figure 3.1. In this Chapter, I first describe how the corner source data are processed and stored in a purposely designed database. The data are analysed to monitor the onset of new traps and to characterise the trap properties. The evolution of the number of pixels affected by traps is presented here along with the trap measurements, including the distribution of the energy losses, their dependence on the CCD temperature, their location and characteristic energy level. An attempt to use the corner source data to measure the fundamental silicon 57

87 all grades grade0 200 counts/pixel/month Figure 3.1: Time evolution of the measured intensity of the radioactive corner sources (half-life of years). parameter ω, the mean energy to generate an electron-hole pair, is described. 3.2 Data processing The data from the corner sources are stored in a specific calibration directory at the University of Leicester Swift data centre (SDC) and are processed separately from the XRT science files. The processing pipeline has been developed by Dr. Mountford, the SDC system administrator and data centre scientist, and it mirrors the tasks included in the xrtpipeline (see Section 1.3.1). In particular, the same event thresholds as the default ones in the xrtpipeline are applied to filter the data and to assign the event grades; the energy of the X-rays in electronvolts, after a correction for the bias, is calculated from the pulse-eight digital number (DN) registered by the ADC, using the gain coefficient stored in the CALDB PC gain file. The bias correction is derived from the corner pixels of single pixel events, the same method implemented by default in the xrtpipeline. The processed corner source data are stored in binary files in a database.

88 59 For every X-ray the following information is recorded: detection time, detector coordinates, measured DN value and event energy (in ev), event grade and CCD temperature. Queries to the database can be sent using Python scripts, with input parameters such as time interval, event grade and minimum and maximum CCD temperature that are used to select specific corner source events. 3.3 Trap detection and history The appearance of new traps in the corner source regions is monitored with an historical analysis of the data. For each pixel, the energy of the Mn-Kα line is derived fitting the X-rays collected over monthly intervals with a Gaussian plus a constant model. The monthly interval was chosen to collect sufficient statistics in the fitted line and an acceptable uncertainty for our purposes in the determination of the time of appearance of a new trap. For the fits, all CCD temperatures are included, and only single pixel events are selected. The output of the procedure consists in monthly energy profiles of the Mn-Kα line along the corner source columns. A dedicated script is used to identify steps in the energy profiles that could be attributed to pixels with energy losses caused by traps. Their location is matched with a database of known trap positions to determine the appearance of newly damaged pixels. Finally, the energy profile of the segment of column affected by the new damaged pixel is visually inspected to validate the detection and to add the new location to the trap database. The trap detection algorithm can identify newly damaged pixels with trap losses equal or greater than approximately 20 ev. The incremental number of damaged pixel with time is shown in Figure 3.2, with data starting from June 2008, the time of the implemented change to the full PC readout window, up to December A faster rate of trap accumulation is seen during 2009 (MET times between and seconds), while at later times the increase in damage seems slower.

89 60 Figure 3.2: Total number of pixels with charge traps causing energy losses equal or greater than approximately 20 ev in the June December 2013 time period. The statistics begins in June 2008, when the PC mode window was widened and the full corner source area started to be telemetered to the ground. A faster increase in trap number is seen during Results The energy losses of the damaged pixels are measured in a two-step procedure. First, the energy of the corner source events is derived in the 20 pixels directly aboveandbelowthedamagelocationbyfittingthemn-kαlinewithagaussian plus a constant model. Second, the line energy profiles along the column above andbelowthe damagedpixel arefittedusing oneofthese threemodels: alinear function, a quadratic function or an exponential function. The linear function is the most common choice for the segment of column below the damaged pixel or when the damage is close to the edge of the corner source illuminated area, and only a limited number of measurements are available above or below its location; the quadratic function usually provides a better fit for segments of column clearly affected by CTI losses; the exponential function is generally the preferred model in cases presenting the characteristic recovery of the energy losses along the column (discussed in detail in Section 3.4.2), as it provides a significantly improved fit. A representative sample of the variety of trap depths, their location and the employed fits function is presented in Figure 3.3. The distribution of energy losses measured using 6 month long intervals since the trap onset, using only single pixel events and applying a 65 C to

90 Figure 3.3: Energy losses for a sample of damaged corner source pixels. 61

91 62 Figure 3.4: Distribution of trap energy losses measured over 6-month long integration epochs since the trap onsets, using corner source single pixel events only and selecting CCD temperatures between 65 C and 55 C. 55 C temperature selection are presented in Figure 3.4. Energy losses of less than approximately 20 ev in damaged pixels are difficult to measure, as they are comparable to the statistical uncertainty in the Mn-Kα line energy measurement and to the spatial non uniformity of the detector response due for example to hot pixels. For the sample of Figure 3.4, the median measured loss of energy is 56 ev; 70% of damaged pixels have trap losses between 20 and 80 ev, but in 17% of cases the losses are higher than 100 ev: the cluster of traps in the most damaged pixels can capture 30 or more electrons, if one assumes a value of 3.65 ev for the ionisation energy parameter ω Temperature dependence The effects of the temperature on the measured trap losses in observations of extended sources as the Tycho and Cas A SNRs, or in the instrumental nickel K-α line averaged over the detector were discussed in Section 2.4. With the rich corner source data set the investigation can be refined to the individual pixel level. For this analysis, trap energy losses were derived integrating data over a period of 2 years since the trap onset was detected, and selecting single pixel events only. Measurements were derived using temperature intervals of 2 degrees in the range T= 65 C to 55 C.

92 63 As expected, the majority of damaged pixels in the corner source regions presents a temperature dependence that reflects what is seen in the Tycho and nickel data sets. In these cases, the thermally generated noise partially fills the traps at high temperatures, resulting in lower energy losses. The charge losses are higher at colder temperatures, but in some cases the increase flattens off at the coldest settings (Figure 3.5, top panels). The flattening can be observed if saturation in the pixel has been reached and there are no additional empty traps in the cluster(that is, the number of captured electrons equals the number of charge trapping sites in the cluster) or if some of the traps in the cluster are frozen, as their emission time becomes longer than a frame exposure and readout time. Such a long emission time implies (Equation 1.12) a trap energy level of at least E t 0.4 ev (compatible with both the E centre and the Divacancydefect, seetable1.1)foratleastafractionofthetrapsinthecluster. On the other hand, an absence of a flattening at the coldest temperatures implies that the captured charge is re-emitted on shorter timescales, as is the case of the Multivacancy (E t = 0.21 ev) and the A centre (E t = 0.19 ev) defects. In a second group of pixels the measured energy losses increase with temperature (Figure 3.5, middle panels). In these cases, even at the highest temperatures, thethermallygeneratednoiseislikely toolowtoaffect thenumber offree traps in the pixels, while some traps become frozen at relatively high temperatures, as characteristic of the deeper defects (E t 0.45 ev, Equation 1.12) such as the P-V trap. Finally, in a small subset of damaged pixels the energy losses do not change significantly with temperature. This flat behaviour can be explained by assuming the stability in the intensity of the generated thermal noise and the value of the traps capture and emission times over the investigated temperature range. Alternatively, the cluster could be composed of a mix of shallower and deeper trap types resulting in an overall balancing of the temperature effects Recovery effect and trap energy levels The analysis of the corner source data evidenced cases of damaged pixels for which the measured X-ray energy appears to be gradually and partially restored the farther above the trap the photon is detected. This behaviour indicates that

93 Figure 3.5: Temperature dependence of the energy losses in corner source trapped pixels. (Top panels) - For the majority of damaged pixels the losses decrease at the warmer CCD temperatures, as the thermally generated noise partially fills the traps in the damage cluster. In some cases (top right panel) a flattening is seen at the coldest temperature as some of the captured charge is frozen. (Middle panels) - The captured charge of these pixels starts to freeze at T 60 C, resulting in a reduction of the charge losses at colder temperatures. (Bottom panels) - In a small number of cases the energy losses are constant across the investigated temperature range. 64

94 65 during a frame readout process the trap sites are partially filled by electrons before the charge cloud of the detected X-ray photon is transferred over the damaged site. This unexpected recovery effect is also observed in Tycho and Cas A trap mapping data sets as well as from the laboratory analysis of the traps conducted on the proton damaged CCD. Some interesting examples of this effect from corner source data are shown in Figure 3.6. In each case, the data were filtered using three distinct temperature intervals centred at T= 56 C, 60 C and 64 C and selecting only single pixel events. The fraction of recovered signal and its rate is a function of temperature, with a more limited and gradual recovery as the CCD temperature is lowered. The influence of temperature is more evident in some cases (e.g. pixels (62,31) and (68,10), top left and right panels), while in other pixels the recovery is more similar at all investigated temperatures (middle panels, pixels (79,12) and (595,42)). In the bottom left panel an example is shown of a recovery from a damage cluster in column 26 that appears to be extended over two pixels. Finally, in the bottom right panel, a case is shown of a first steep energy step in the pixel adjacent to the damage, followed by a more gradual recovery along the column; in this case, the damage cluster in the pixel (25,13) is likely partially located in the volume where the charge from the detected X-ray is generated (see also the following Section for a more detailed discussion of the size and location of the damaged region in these pixels), while other traps are located in the volume of pixel where the charge is transferred. In optical CCDs affected by radiation damage, a behaviour analogous to the recovery effect presented here is the observed tails of deferred charge caused by pixels affected by traps. In the optical images, the charge generated by the detection of the optical photons is first captured by the traps and then released at a later time during the frame readout transfer. The observed charge trail has an exponential shape, as the charge is re-emitted with a probability P e that is a function of the emission time scale τ em (Equation 1.12). The optical analogy and the influence of the CCD temperature on the restored charge have led me to interpret the observed recovery in terms of trapped and de-trapped charge generated from thermal noise and hot pixels during each CCD row transfer of the readout process, assuming an instantaneous capture and a release timescale given by the emission time constant. In detail, defining N 0 as the initial number of occupied trap energy levels in a damaged cluster

95 Figure 3.6: Recovery effect. Damaged pixels for which the measured X-ray energy appears to be gradually and partially restored the farther above the trap the photon is detected. From top to bottom, the cases in columns 62, 68, 79, 595, 26 and 25 are shown here, with the measured event energy plotted as a function of the row detector coordinate (DETY). Data are extracted selecting three distinct CCD temperature intervals centred at T= 56 C, 60 C and 64 C. The recovery is generally faster at higher temperatures (top panels), while in other cases the recovery appears more gradual and similar at the three investigated temperatures (middle panels). The recovery is also seen for damage extended over two pixels (bottom left panel) and in a case where the damage is partially located in the volume of the pixel where the charge from the detected X-rays is generated and collected (bottom right panel). 66

96 67 and τ em the emission time constant, the number of filled levels at time t is n(t) = N 0 e t τem (3.1) so that, as a function of the row transfers, if BG is the average thermally generated background mean signal, the filling of the damage is given by Row 0 n(0) = 0 Row 1 n(1) = BG Row 2 n(2) = n(1)e 1 τem +BG Row 3 n(3) = n(2)e 1 τem +BG = BGe τeme 1 1 τem +BGe 1 τem +BG = BG(e 2 τem +e 1 τem +1) (3.2) After t row transfers, from Equation 3.2 the number of filled trap levels is, n(t) = BG t i=0 1 = BG e i τem ) (e 1 t+1 τem 1 e 1 τem t+1 τem n(t) = BG 1 e 1 e 1 τem (3.3) where the formula for the sum of a geometric series was applied. Equation 3.3 is the function of the number of transfers t that I use to model the measured energy recoveries. The energy profiles for the sample of columns presenting a clear recovery effect were derived following the standard procedure, selecting only single pixel events and applying the three temperature filters described above. For each pixel in the column, the line energy was derived by fitting a Gaussian plus a

97 68 constant function to the detected X-ray photons. The IDL MPFIT package was then used to derive the best fit of the observed recoveries using Equation 3.3 to determine τ e. The physically interesting parameter of the profile fitting is the emission time constant, as it is related to the trap characteristic energy level below the conduction band of Equation The observation of a recovery in itself poses limits on the emission time values that are expected to be of the order of a few to tens of the transfer time. In fact, traps with emission time much shorter than the transfer time leave the signal unaffected, as the captured charge is released before the signal electrons are transferred to the next pixel, while a very long emission time would result in the freezing of the trap, as is the case of CCDs cooled to 100 C or less to reduce the CTI. In Equation 1.12, the number of effective states in the conduction band N C and the thermal velocity v th are given by Janesick (2001): ( ) 2πm 3/2 N C = 2 e kt (3.4) h 2 ( ) 1/2 3kT v th = (3.5) that combined are a simple function of the CCD temperature T m e v th N C = T 2 (3.6) while the product σx for the most common traps are reported in Table 1.1. The temperature interval T=[-64C, -56C] was selected in the derivation of the trap energy levels as it s the one with the higher X-ray photon statistics. A sample of cross section values were used in the calculations, ranging from σx = , characteristic of the Divacancy defect, to σx = , the cross section of the Si-A centre. Equation 1.12 yielded trap energy levels in the damaged pixels between 0.25 and 0.35 ev (Table 3.1). These values should be interpreted as the resultant of the trap types in the damaged pixels, as it is likely that the irradiation caused the formation of damage clusters constituted of different type of defects. Therefore, a measured E t value of 0.35 ev could be due for example to a combination of E-centre defects, characterised by an energy level E=0.46 ev, and lower energy traps as the Carbon-Vacancy-Oxygen

98 69 (X,Y) E(σX = ) E(σX = ) E(σX = ) (003,018) 0.263± ± ±0.003 (025,014) 0.299± ± ±0.008 (025,551) 0.272± ± ±0.003 (026,008) 0.280± ± ±0.003 (026,029) 0.285± ± ±0.015 (029,038) 0.284± ± ±0.007 (034,016) 0.275± ± ±0.002 (062,031) 0.271± ± ±0.002 (065,560) 0.256± ± ±0.005 (068,010) 0.284± ± ±0.003 (074,582) 0.249± ± ±0.004 (079,012) 0.300± ± ±0.009 (550,574) 0.280± ± ±0.009 (571,038) 0.286± ± ±0.009 (573,542) 0.269± ± ±0.011 (578,524) 0.278± ± ±0.007 (595,042) 0.254± ± ±0.005 Table 3.1: Mean energy levels of the traps in the damaged pixels, as derived from the best fit of the energy profile recoveries, for a sample of representative values of σx. complex (C-V-O) with E t = 0.38 ev and the multivacancy with E t = 0.21 ev Location and size of the damage cluster The analysis of the corner source data provides constraints on the physical size of the trap clusters that cause the observed charge losses. In all but a handful of cases the step in the energy profile measured using the Mn-Kα line occurs in a single pixel, where the volume of the damage cluster is contained. In the few exceptions, seen in columns 15, 24, 26, 552, 565 and 578, the charge losses are detected at most in two adjacent pixels along a column, and likely consist of damage generated right at the boundary of the two pixels. The location of the damage within a pixel can also be inferred from the measured energy profiles along the columns. The step observed in most cases (see for example the energy profile of column 256, Figure 2.5) implies that the X-ray photons detected in the segment of column above the damage location are all affected by the traps in the damaged pixel. The cluster must

99 70 Figure 3.7: The corner source analysis revealed the presence of several isolated pixels with a lower measured Mn-Kα line energy with respect to the neighbouring pixels, as in the case of the damaged pixel (74,548), left panel, and (545,544), right panel. therefore be located in the volume of the pixel defined by the potential well of the electrodes where the electrons are confined during the charge transfer process (see Figure 1.2 for the buried channel case). In other columns, instead of the step, isolated pixels are seen with a measured line energy well below the level of the neighbouring pixels, as shown for example in Figure 3.7. The corner source analysis revealed a total of more than 40 pixels (out of a total of approximately 16 thousands illuminated by the radioactive sources) with this behaviour. In the majority of cases the offset was detected since 2008, when all the corner source events started to be telemetered to the ground. This leaves open the possibility that the effect is due to non-uniformities in the detector response already present at launch, caused for example by manufacturing defects. In a fraction of cases the offset is only seen in later data and therefore can be attributed to the energy losses caused by radiation damage. In these instances presenting an isolated drop in measured energy, only the photons detected at the damage location lose energy, while the X-rays detected in other pixels of that column remain unaffected. The observed behaviour suggests that the damage is not located within the volume defined by the potential well of the electrodes, but instead has occurred deeper in the pixel, where the Mn-Kα events from the corner sources are absorbed. With a silicon absorption coefficient of µm 1 at an energy of 5.9 kev, assuming a thicknesses of 27 µm for the depletion region and 80 µm for the field-free (FF) region (Godet

100 71 et al., 2009), approximately 60% of interactions are expected to occur in the depletion region and 30% in the FF region, while only in 1.5% of the cases the absorption occurs at a depth of less than 1.5 µm (the buried channel depth). 3.5 A new method to derive the mean electronhole pair creation energy in silicon The fundamental parameter ω is the photon energy required to generate an electron-hole pair in the semiconductor material through the photoelectric effect. When absorbed, an X-ray photon of energy E creates an average number of e-h pairs N = E/ω as defined in Equation 1.1. The value of the ionisation energy ω in silicon can be derived theoretically and with Monte Carlo simulations and is found to be dependent on both energy and temperature. In particular, ω is calculated to be an increasing function of photon energy and non linearities are predicted at the silicon absorption edges (Fraser et al., 1994), while temperature variations are expected in theoretical models (Mazziotta, 2008) because the energy gap between the valence and the conduction band is a function of temperature (Alig et al., 1980). The determination of the functional dependencies of the ionisation energy is important to correctly model the energy response of the detectors used in X-ray astronomy, and ultimately, more precise physical models guarantee an higher scientific return from X-ray missions. For this reason, many laboratory measurements of ω in silicon using various techniques and detectors have been attempted and reported in the literature. A history of published values of ω can be found in Fraser et al. (1994). The temperature dependence was confirmed experimentally bypehl etal.(1968), using a 57 Co sourcetogenerate121.97kev gamma rays and Li-drifted silicon detectors, and more recently by Lechner et al. (1996) at soft X-ray energies, using synchrotron radiation and a silicon n+n-p+ diode at detector temperature 140 K and 300 K and by Lowe & Sareen (2007), who found a gradient of ± % K 1 using a silicon p-i-n diode and 5.9 kev X-rays over the range K. Measurements of the energy response of silicon using CCDs and a synchrotron source in agreement with the predictions from Fraser et al. (1994) are reported by Owens et al. (1996). On the other hand, Gullikson et al. (1996) derive an upper limit of 0.5% to

101 72 the discontinuity at the L 2,3 edge, below the 5% level predicted by Fraser et al. (1994); moreover, Scholze et al. (1998), in contrast to other experimental and theoretical results, find a constant value of ω = 3.66 ± 0.03 ev at room temperature, independent of energy, over a photon range [ ] ev using a syncrotron radiation source and silicon photodiodes; the inconsistent outcome could be due to the different method (photocurrent rather than pulse-height measurements) used in the latter works. A novel method to derive the value of the electron-hole creation energy in silicon that makes use of the energy losses caused by traps measured from the corner source data set is presented here. The underlying idea is that when the charge cloud generated by the absorption of an X-ray photon is transferred through pixels damaged by radiation during a frame readout process, only an integer number of electrons can be captured by the traps, leading to the quantisation of the energy losses. The radiation damage of the CCD makes therefore the XRT a unique laboratory in space to measure the value of ω at the energy of the Mn-Kα peak from the corner source X-ray photons at the operational temperature of the XRT CCD Modified χ 2 minimisation technique The value of ω can be derived from energy loss measurements through an adaptation of a χ 2 minimisation calculation. The measured energy losses D i (in ev) are related to the number of captured electrons N i in the damaged pixels by ω : D i = N i ω. The χ 2 function is therefore: χ 2 = N (D i N i ω) 2 σ 2 i=1 i (3.7) where N is the total number of pixels with measured trap depths and σ i are the trap depth errors. The number of captured electrons N i and ω can not be directly derived by minimising χ 2 as N is degenerate with ω. One can instead reverse the problem and let ω vary within a range of explored values and calculate the corresponding χ 2 minimum. In practice, this is obtained by selecting for each damaged pixel thenumber ofcapturedelectrons N i thatmoreclosely approachesthemeasured losses D i given the chosen value of ω guess :

102 73 Figure 3.8: (Left panel) - The χ 2 minimisation function of Equation 3.8, calculated for a set of 100 traps of energy losses randomly generated from a uniform distribution of values in the range ev, is larger for increasing values of ω. (Right panel) - The same function divided by ω 2 guess. Min(χ 2 ) = = N (D i N i ω guess ) 2 σ 2 i=1 i ( N (D i round σ 2 i=1 i ) D i ω guess ω guess ) 2 (3.8) where round is the nearest integer function. The Min(χ 2 ) function is nonmonotonic but the overall trend is to grow larger for increasing ω guess, as the ω guess value constrains the maximum deviation of N i fromthe measured D i values. This can be seen in Figure 3.8, left panel, where Min(χ 2 ) was derived for a randomly generated, uniform distribution of one hundred traps with depths between 20 and 100 ev and ω set to vary in the [1-3] ev range. This bias is compensated by dividing Min(χ 2 ) by ωguess 2 as shown in the right panel of Figure Numerical simulations The efficacy of the minimisation of the modified χ 2 function to measure the value of the electron-hole creation energy in silicon was investigated through numerical Monte Carlo simulations. The simulations consisted in series of synthetic data sets, randomly generated based on a preliminary set of trap depth

103 74 measurements obtained from corner source data collected over periods of 6 months, which included all CCD operational temperatures. The simulations allowed to determine the probability distribution of the parameter ω min, estimated using the technique of minimisation of the modified χ 2 function under a set of controlled scenarios. In detail, the following cases were evaluated: Random trap energy losses, generated using a uniform distribution of ω valued in the range [3-4.5] ev; Trap energy losses generated assuming a fixed ω value of 3.7 ev, based on the input distribution of preliminary trap depth measurements; Trap energy losses generated assuming a fixed ω value of 3.7 ev, based on the input distribution of preliminary trap depth measurements with uncertainties scaled by error coefficients varying between 0.3 and 1; The simulations consists of the following steps. A set of integer numbers n i corresponding to the number of captured electrons is randomly generated, by drawing from the input probability distribution of the preliminary trap depth measurements and assuming a value for ω. To reproduce the uncertainties of the measured trap depths, the n i values are then offset by quantities e i derived as follows: firstly, a random set of errors σ i is generated from the distribution of 1-σ uncertainties of the measured trap depths; secondly, the e i values are randomly generated from normal distributions of standard deviations σ i. The simulations were coded in IDL, using the built-in genrand routine, that generates random numbers from an arbitrary input distribution by inverting the cumulative probability function. Figure 3.9 illustrates the random generation process. The top panels present the distribution of the preliminary measurements of the trap depths and the distribution of the number of captured electrons n i randomly generated from the input depths values and assuming, in this case, a fixed ω value of 3.7 ev; the bottom panels present the input distribution of measured trap depths uncertainties, the randomly generated σ i distribution and the distribution of offsets e i applied to the n i. As introduced earlier, in the first run of simulations the probability distribution of the ω min parameter was determined for an electron capturing process in the case of a random distribution of ω values; this was achieved by using

104 75 Figure 3.9: Example of a distribution of trapped electrons (top panel) and errors (bottom panel) generated in IDL and used in the simulations to test the validity of the χ 2 technique. These distributions were generated using the genrand routine, that generates random numbers from an arbitrary input distribution by inverting the cumulative probability function. The input distribution consisted of the preliminary trap depth measurements and a value of 3.7 ev was assumed for ω. a different ionisation energy value in each trial, generated from a uniform distribution within the range of values [3-4.5] using the IDL routine randomn. Contrary to what one could expect in the case of a random process the ω min probability distribution is not uniform (equal probability for all the possible ω values); this is due to the fact that the form of the modified χ 2 function presents a sinusoidal behaviour of higher frequency at lower values of ω (see Figure 3.8). In fact, the probability distribution of ω min for a random electron capturing process, obtained from a total of ten thousand simulation runs and shown in Figure 3.10, was found to have a power law functional form, y = αx β, with best fit parameters α = 3720±722 and β = 1.85±0.14. The second set of simulations aimed at establishing if the ionising energy parameter could be confidently derived given the distribution of preliminary

105 76 Figure 3.10: Probability distribution of ω min derived from numerical simulations, in the case of a random electron capturing process, with ω allowed to vary with uniform probability in the range [3-4.5]. trap depth measurements and uncertainties. A total of 10 thousand synthetic trap data sets was generated assuming a fixed ω value of 3.7 ev, and ω min estimated minimising the modified χ 2 function. The probability distribution of ω min obtained from this set of simulations is displayed in Figure 3.11, left panel, and its deviations from the case of a random trapping process is shown on the right panel. The input ω value of 3.7 ev is recovered within ±0.1 ev in only 14% of the cases. Figure 3.11: (Left panel) - Probability distribution of ω min derived from Monte Carlo simulations, using the preliminary trap measurements as the input distribution and fixing ω to a value of 3.7 ev, recovered within ±0.1 ev in only 14% of the cases. (Right panel) - Deviations of this probability distribution from the one obtained from the case of a random electron capturing process.

106 77 The low significance of the ω parameter estimation obtained from simulations based on the preliminary trap measurements indicates that the trap depth uncertainties are too large for a statistically significant measurement. A third set of simulations was therefore implemented, introducing a scaling coefficient to adjust the magnitude of the errors. The goal in this case was to derive the improvement needed in the corner source analysis to successfully measure the ionising energy value and to estimate the confidence limits on the model parameter. The simulations consisted of 10,000 trial runs, fixing ω to 3.7 ev and using error scaling coefficients varying from 0.9 to 0.3. The minimum of the modified χ 2 function was searched in the parameter range [3-4.5]. The trials showed that the procedure yielded a ω value within [ ] in 58%, 87% and 100% of cases if the errors were reduced using a scaling coefficient of 0.7, 0.5 and 0.3 (see Table 3.2). The probability distributions of ω min obtained with scaling coefficients of 0.8 and 0.5 are shown as examples in Figure Table 3.2: Simulations to derive the confidence level in the estimate of the ionisation energy in silicon by minimising the modified χ 2 function. The trials were based on the distribution of the preliminary trap depth measurements and their uncertainties, fixing ω to 3.7 ev. An error coefficient between 1.0 and 0.3 was tested to scale the preliminary trap depth uncertainties. The percentage of trials when ω was recovered using the χ 2 minimisation technique within the ranges [3.6,3.8] and [3.4,4.0] is reported here as a function of the trap depth error level. Error coefficient ω = [ ] ω = [ ] % 38% % 39% % 41% % 49% % 64% % 89% % 99% % 100% The precision in the trap measurements can be improved using various filtering criteria on the preliminary data set such as an increase of the integration time, a temperature selection and a filtering of the worst trap measurements. Each strategy has its potential drawback: for example, the exclusion of traps

107 78 Figure 3.12: Probability distribution of ω min derived from Monte Carlo simulations, fixing ω to a value of 3.7 ev, and reducing the uncertainties of the preliminary trap measurement by error scaling factors. (Left panel) - Scaling factor of 0.8: the ionisation energy parameter is recovered within ±0.1 ev in 21% of cases. (Right panel) - Scaling factor of 0.5: the ionisation energy parameter is recovered within ±0.1 ev in 87% of cases. with the least convincing energy loss measurements (for example due to large uncertainties in the energy response of individual hot pixels next to the trap location) reduces the size of the sample used in the statistical derivation of ω; or a more restrictive CCD temperature selection, to avoid its effect on the number of captured electrons by traps, implies lower X-ray event statistics. In the following section the results achieved from various selections and trap measuring techniques are presented Results Filtered trap measurements The preliminary dataset used in the simulations consisted of trap energy losses measured integrating 6 months of data since the appearance of the traps, and included all CCD operational temperatures. The trap losses were estimated by fitting a set of functions along the segments of columns above and below the damaged pixels, as described previously in Section 3.4. In a few cases the quality of the data or the fits were poor due to the damaged pixel being at the edge of the region illuminated by the corner sources or because of the presence of hot pixels close to the trap that caused large non uniformities in the energy profiles along the columns. These traps were

108 79 Figure 3.13: (Left panel) - The modified χ 2 minimisation procedure does not reliably estimate the value of the ionising energy parameter using the set of trap measurements after the exclusion of the data with poor fits has been applied. The technique returns a series of local minima of similar significance in the investigated range of parameter values. (Right panel) - Probability distribution of ω min from Monte Carlo simulations: the input ω guess value is not correctly estimated to a significant confidence level. removed from the initial sample. The application of the modified χ 2 minimisation technique in this case provided a formal ω min = 3.73 ev, but other minima with a similar reduced χ 2 values are present, as can be seen in the left panel of Figure The result was therefore tested with a simulation run consisting of 10,000 trials, using as input distribution the filtered trap depth measurements and assuming an ω guess value of 3.7 ev. The probability distribution of ω min from the simulations (Figure 3.13, right panel) confirmed that the input ω guess value could not be correctly estimated to a significant confidence level: discarding the data with the worst fits did not provide a sufficient reduction of the uncertainties but had the negative impact of reducing the size of the sample used to attempt the derivation of the ionising parameter. Temperature selections The XRT CCD is operated at temperatures between 80 C and 45 C (Figure 3.14). The application of a temperature selection to the data has a twofold benefit: it limits the effects of temperature on the charge lost to traps (for the majority of damaged pixels in the corner source regions the trap losses vary with temperature, in many cases by more than 20 ev when the temperature varies between 65 C and 55 C; for a detailed discussion see Section 3.4.1) and on

109 80 Figure 3.14: XRT CCD operational temperatures over a period of 6 years. the parameter ω itself, as the energy required to generate an electron-hole pair weakly depends on temperature. Such a selection is instead detrimental in the reduction of the statistical richness of the data. Temperature intervals centred at the peak of the XRT distribution (T 60 C) were attempted: I1=[-61.0,-59.0], I2=[-62.0,-58.0], I3=[-64.0,-56.0], I4=[-65.0,-55.0]. The XRT CCD operates approximately 22%, 42%, 73% and 84%of thetime inthe fourchosen intervals. As themain cause ofthe measured temperature variations of the charge losses is likely the thermally generated electronic noise, it is interesting to assess its level and its fluctuations in the adopted temperature intervals. The variations in noise values can be evaluated from the XRT bias maps taken during the spacecraft slews between targets. From the XRT bias images acquired during 2013, the bias level is found almost constant, at a value of 100 ev for CCD temperatures within the interval I1, while the bias variations are large in the temperature range I4, with an average value of 30 ev at T= 65 C and 150 ev at 55 C. Moreover, it is important to notice that the median bias value also varies with time, increasing by 15 ev per year at T= 55 C during the period. The narrower I1 temperature selection did not prove successful in the search of ω. As in the case of the preliminary sample, the measurements with low quality and bad fits were excluded. The χ 2 minimisation technique was applied on the final dataset but no clear minimum and corresponding ω value was found. The outcome of the minimisation procedure is presented in Figure 3.15,

110 81 Figure 3.15: (Left panel)- Results of the analysis the trap measurements for the temperature selections of interval I1=[-61.0,-59.0]; the χ 2 minimisation was not successful, as it yielded several minima of approximately the same magnitude within the range of ω values under investigation. (Right panel) - Distribution of the errors in the trap depth measurements with no temperature selection applied (black histogram, median error of 2.1 ev) and with the selected interval I1=[-61.0,-59.0] (red histogram, median error of 2.9 ev). The larger errors are a consequence of the lower photon statistics after the temperature filtering was applied. left panel, for the case where traps with measurement errors larger than 4 ev were excluded. The lack of a significant detection of the ionising parameter was confirmed with Monte Carlo simulations (10,000 trials). The null result can be attributed to the lower photon statistics after the temperature selections. Firstly, the quality check on the data and the fits leads to the discarding of a large number of traps from the initial sample of damaged pixels, resulting in fewer trap depth measurements used to derive ω. Secondly, the reduced number of photons results in larger uncertainties in the measured energy losses. These effects are shown as an example in the right panel of Figure 3.15, that compares the error distribution of the trap measurements without temperature selections applied and with the selection of the temperature interval I1=[-61.0,-59.0]; the median error increases from 2.1 ev to 2.9 ev after the temperature filtering. The most convincing result was achieved using the I2=[-62.0,-58.0] selection. The trap loss measurements of 96 radiation damaged pixels were included in the sample after the quality check on the data and the fits were applied, resulting in a median value of the measurement errors of 2.3 ev. The electron-hole pair creation energy value derived from the χ 2 minimisation was ω min = 3.73 ev

111 82 (see Figure 3.16, left panel). Extending the search to an ω range of [3-4.5] did not change the outcome of the minimisation procedure. The significance of the result was evaluated through numerical simulations. Trap depths and errors were generated based on the measurement distributions of the I2=[-62.0,-58.0] sample. Ten thousand trials were executed, minimising the modified χ 2 function to derive ω. The input value of 3.7 ev was retrieved within ±0.1 ev in only 13% of the cases, a low value indicative of the low significance of the reported measurement of ω, that can therefore be interpreted as statistically not significant. The main difficulty in the parameter derivation appears to be once again the magnitude of theuncertainties: if theerrors were reduced by a factorof 2 inthe simulations the likelihood of retrieving the correct value of ω within ±0.1 ev rises from 13% to 61%. A more restrictive selection of data and fit quality was therefore attempted to try to lower the size of the errors, excluding the shallower traps (with losses of less than 40 ev) and only including measurements from the most robust fits. This resulted in a smaller sample of 51 measured trap depths from the original 96. The modified χ 2 function minimisation on the new dataset yielded ω min = 3.72 ev. The ordered sequence of measured trap depths from the highest quality sample is shown in Figure 3.16, right panel. Once again, the simulations based on the input distribution consisting of the 51 best trap measurements did not support the statistical significance of the parameter Figure 3.16: (Left panel) - Results of the analysis for data selected within the temperature interval I2=[-62.0,-58.0]; ω min = 3.72 ev was found through χ 2 minimisation. (Right panel) - Ordered sequence of trap energy losses from the selection of the best quality fit. The horizontal red lines are spaced by ω = 3.72 ev.

112 83 estimatation, as the input value of 3.7 ev was correctly derived within ±0.1 ev in only 12% of the cases. While the error distribution was improved, the stricter selection criteria reduced too drastically the size of the trap sample to measure the ionising parameter with statistically acceptable confidence. Extended integration times The measurements presented so far were based on corner source events collected over a period of six months since the onset of the trap was detected. This integration time was chosen to accumulate a sufficient number of X-rays to derive the energy of the Mn-Kα line with a Gaussian fit in individual pixels and because, within a 6-month interval, the chance of additional damage caused by radiation is low, resulting therefore in a stable value of the energy measured over the period. But this is not always the case: if a new trap appears in a colum below a known damaged site during the 6-month integration time, the measured energy of the Mn-Kα photons will be affected and the original trap depth measurement compromised. The lack of a statistically significant detection of the ionisation energy using 6-month intervals highlighted the necessity to optimise the integration times to guarantee stable trap depth measurements and to increase the photon statistics to reduce the size of the uncertainties of the trap losses. This was achieved by implementing an historical analysis that monitored the energy response of the pixels just below known traps using monthly measurements of the Mn-Kα line energysincethetraponset. TwoexamplesareshowninFigure3.17. Ontheleft panel, the time evolution analysis evidences the appearance of two new large traps in column 571 below row 38 in two distinct episodes at T s and at T s; on the right panel, a case of stable energy losses since the appearance of the trap: the integration time can be extended to the full investigated time interval to increase the photon statistics. The accumulation of X-ray photons from the corner sources was therefore extended to the longest period of stability of the measured energy derived from the historical analysis of the traps. This resulted in an integration time of over two years for a fifth of the traps, and a median integration lenght of 17 months. The temperature intervals I1=[-61.0,-59.0], I2=[-62.0,-58.0], I3=[- 64.0,-56.0] were selected to filter the data and to attempt the derivation of ω.

113 84 Figure 3.17: Historical analysis of the energy losses. (Left panel)- The evolution of the measured energy in the pixel just below the known trap shows two large drops as new traps in the same column appear below its location. (Right panel) - The energy in the pixel just below the known trap is stable since the trap onset. The improved statistics fractionally lowered the size of the errors (for example, the median error σ median was 2.15 ev for the I3 interval) but the modified χ 2 minimisation technique still did not result in a significant measurement of ω. A possible explanation for the lack of a detection of the ionising parameter is the systematic error introduced by the evolution of the detector spectral response when long integration intervals are considered. For example, the value of the gain coefficient, the critical parameter in calculating the X-ray event energy, evolves with time (Figure 2.4) and is a function of CCD temperature. These dependencies are modelled with linear functions in the CALDB gain file, but an error in the gain estimation of just 1 in a 1000 results in uncertainties of a few evs at the Mn-Kα energy, of the same order of the ω parameter itself. Moreover, the historical analysis does not have the sensitivity to detect energy variations caused by the generation, or the annealing, of the shallower, single-electron traps during the integration time, that nevertheless modify the amount of lost energy measured in the damaged pixel. As already discussed, the XRT electronic thermal noise is also not constant on long timescales. Another effect limiting the data quality is the decay of the intensity of the radioactive 55 Fe sources (Figure 3.1). Because of this, longer integration times are required for recently generated traps; as an example, the X-ray collection time interval needs to be raised from six months for a damaged pixels detected in 2008 to two years for a defect generated in 2013 to achieve an equal level of

114 85 photon statistics to measure the energy losses. The longer time intervals aggravate the uncertainties in the measured photon energy caused by the instabilities of the detector response described above. The derivation of ω was therefore attempted by including in the analysis the damaged pixels with the highest photon statistics, that is, the ones detected in the years 2008 and 2009 only. The temperature interval I2=[-62.0,-58.0] was chosen and only single-pixel events were selected. A total of 61 damaged pixels measurements were included in the χ 2 minimisation procedure, that yielded a value of ω min = 3.73 ev. The significance of the result was tested through simulations, with ten thousands test runs based on the trap depths measurements and errors of the 61 damaged pixels detected in 2008 and 2009 and assuming an input ω guess value of 3.7 ev. The correct parameter value was retrieved within an interval ±0.1 ev in just 15% of the cases, a probability slightly higher than in previously analysed samples but still not sufficient to claim the statistical significance of the detection. Additional, more stringent filtering were applied by using upper thresholds of 2.0 and 2.5 ev to the errors of the energy loss measurements. The ω values obtainedwithχ 2 minimisationusingtheseselectionswereclose(within0.01ev) to previous derivations. The sorted trap depths and the minimisation result of ω min = 3.73 obtained with a 2.5 ev threshold filtering are presented in Figure Unfortunately, the simulations could not provide the statistical support to the result: the applied threshold improved the error distribution at the expense of the size of the sample, resulting in too few measurements to derive ω with the necessary statistical significance. With ten thousands simulation runs, the correct value of ω was only retrieved in 14% of cases within an interval of ±0.1 ev Analysis of the XMM calibration data to derive the ionisation energy of silicon The XMM-Newton satellite is an European Space Agency X-ray mission launched from French Guiana in December 1999 in a highly elliptical orbit with a period of 48 hours. The XMM payload consists of three X-ray telescopes with imaging cameras (the European Photon Imaging Cameras, EPIC) and spectrometers (the Reflection Grating Spectrometers, RGS) and an optical/ultraviolet tele-

115 86 Figure 3.18: (Left panel) - χ 2 minimisation from the sample of damaged pixels detected in 2008 and 2009, with error measurements of less than 2.5 ev, resulted in an ω value of 3.73 ev. (Right panel) - Ordered energy loss values; four pixels with losses greater than 120 ev are not shown in the plot to better evidence the comparison of the measured trap losses with the steps of 3.73 ev (in red) expected based on the ionisation energy value obtained from the χ 2 minimisation procedure. scope (the Optical Monitor, OM). EPIC s imaging cameras are a PN detector (Strüder et al., 2001) and 2 MOS detectors (Turner et al., 2001), MOS1 and MOS2, each consisting of 7 e2v CCD-22, the same type as the one utilised on board the XRT. The spectral response and the evolution of the CTE of the MOS detectors is monitored by the XMM calibration team using 55 Fe radioactive sources that illuminate the camera focal plane and produce strong Al and Mn-Kα lines at 1.5 and 5.9 kev. The analysis of these data has revealed an increase in CTI since the beginning of the mission, caused by irradiation of very soft and high energy protons (Lumb, 2004). A partial recovery in the CTE performance has been achieved by an enhanced cooling of the MOS CCDs in November of 2002, from an initial temperature of 100 C to a colder 120 C setting (Abbey et al., 2003; Sembay et al., 2005); this resulted in a reduction of the parallel CTI by a factor of three and a much slower degradation of the parallel CTI observed since then. Thetrapenergylossesderived fromtheanalysisofthemos 55 Fecalibration sources can be exploited to attempt the measurement of the ionisation parameter ω of silicon, as already described in the case of the XRT corner source data. The analysis of MOS data, with respect to the XRT, has the great advantage

116 87 of the lower and fixed CCD operating temperature. The colder temperature results in a lower instrumental background level, so that fewer thermal noise electrons can fill the trapping sites. The stability of the operating temperature is also beneficial in the MOS analysis, as the uncertainties in the measurement of the temperature dependence of XRT gain coefficient, that can affect the accuracy of the energy scale, don t have to be taken into account. Moreover, the probability of an electron becoming trapped and subsequently released, determined by the characteristic trap capture and emission times, do not vary with time in the MOS case, thanks to the fixed temperature. Overall, the stable and colder MOS camera temperature results in a static configuration of the trap properties during the calibration observations, while for the XRT the number of empty trapping sites is dynamically altered by the changing temperature, affecting the measurement of the trap losses. The disadvantage of the MOS dataset is that calibration data are only taken at specific times during the mission, while XRT corner source data are continuously being collected in Photon Counting mode, so that the number of calibration source events per pixel in the time interval chosen for the trap analysis is generally lower for XMM than for Swift. In this Section, I present the results of a feasibility study into measuring the ionisation energy parameter ω using MOS calibration data. Calibration data sample and processing The MOS1 and MOS2 databases, that as of October 2014 include observations taken from the start of the XMM mission up to the first few months of 2014, were searched for calibration source data. The query resulted in a total of 1207 MOS1 and 1216 MOS2 calibration source datasets, with the detectors operated in Full Window mode and a total exposure time of approximately 12.7 Ms for MOS1and12.2MsforMOS2. Inmoredetail, withthemos1camera, 1.6Msof calibration observations were accumulated during the first year of the mission (year 2000), the minimum annual exposure occurred in 2003 with a total of 0.46 Ms, while the maximum was reached in 2011 with 2.1 Ms; the year 2000 is the richest in terms of X-ray events statistics from the emission lines, due to the decreasing intensity of the 55 Fe calibration sources with a half-life of years.

117 88 Figure 3.19: (Left panel) - Spectrum extracted from column RAWX=333 of the MOS1 central CCD from the entire 15 year dataset of calibration source observations. (Right panel) - Deep defect detected in column RAWX=69 in pixel RAWY=103 from the analysis of the calibration source data collected during year The calibration source datasets were processed using the SAS software task emproc, that generates an output FITS file of cleaned, calibrated X-ray events. In particular, the energy of the processed X-ray events is in units of ev, but the emproc task was run without applying the CTI corrections. This option was chosen as the CTI values included in the SAS CCF calibration files are, by their nature, a measurement of the average charge loss per pixel across the whole detector, while instead the trap spatial distribution is non-uniform, and the use of an average CTI value could therefore result in an imprecise correction of the charge losses in individual columns. As an example, the spectrum from calibration source observations extracted from column RAWX=333 for the entire duration of the mission is shown in Figure 3.19, left panel, with the characteristic emission lines at 1.5 kev, 5.9 kev and 6.5 kev from Al and Mn-Kα and Mn-Kβ. Trap analysis For the feasibility study the detector at the centre of the MOS1 camera, CCD1, wasselected. Trapsweresearchedforinthe 55 Fecalibrationsourceobservations accumulated in the years 2005 and 2011; the rationale for this selection was to maximise the density of emission line X-ray events and to identify as many damaged pixels as possible. With these criteria in mind, 2005 was chosen as it corresponds to the statistically richest year after the camera temperature

118 89 was lowered to 120 C in November 2002, while 2011 is the richest amongst the more recent years, when the prolonged, continuous exposure to the space radiation had generated a large number of defects in the affected pixels. Even with the optimised selection, it became soon clear that the low X-ray statistics would be the critical factor in the analysis. In fact, the average number of single pixel events (PATTERN 0 events, in the XMM nomenclature) per pixel from the Al-Kα line, the strongest emission line from the calibration source, is only equal to 3.3 when merging all 2005 data (1.3 Ms of total exposure time) and 1.2 in 2011 (total exposure of 2 Ms), making the identification and the localisation of a damaged pixel very challenging. The procedure to identify the traps and measure their trap losses is analogous to the one used for the XRT corner sources, and is briefly described next. Firstly, the 55 Fe calibration source observations are merged, selecting only single pixel events and events recognised as good by the SAS software (FLAG = 0) and extracting data for each detector RAWX column. Events of adjacent 20 pixels are incrementally merged along the columns to fit the Al-Kα emission line using the same IDL code described for the XRT analysis. The measured line energy along the columns allows the identification of large energy losses, of the order of 20 ev or more in 2005, and of 50 ev or larger in 2011 when the analysis is affected by the lower photon statistics. An example of a deep trap, identified in column RAWX=69 in data collected in 2005, is shown in Figure 3.19, right panel. Once a damaged column has been identified, the optimal integration time to precisely localise the damaged pixel and measure the energy losses is derived. Data of the affected column as a function of time is analysed for the entire mission, fitting the Al-Kα emission line in two column segments, typically 50 pixels long, below and above the approximate, preliminary defect location. The measured line energy is inspected to determine the time of the trap onset and its stability, as variations can be introduced by the generation of additional trapping sites in the column after the initial appearance of the defect. Subsequently, all data within the optimal integration epoch is selected, allowing the collection of sufficient X-ray statistics to fit the Al-Kα line in each pixel along the column or, in the worst cases, from the merging of a limited number of pixels. With this detailed analysis the position of the defect can be refined by determining, in the majority of cases, the exact pixel location of the

119 90 damage, or within 2-3 pixels in the case of the shallower traps or in columns with low statistics. Once the refined trap position has been found, its depth is measured. In the case of the MOS data, the recovery effect in pixels above a trap, observed for some defects in the XRT corner source data, is not seen, likely because of the much colder CCD temperature, that results in a lower thermal background noise and in longer trap emission times. Thanks to this, the line energy measured along the columns can be well fitted below and above the trap by a linear function. The trap depth and its uncertainty are thus derived from the linear function best fit parameters. Using this procedure, in the 2005 dataset a total of 62 damaged pixels were identified and their charge losses measured, and 47 more were added with the analysis of the 2011 dataset, for a total of 109 traps; the distribution of these traps energy losses is shown in Figure The trap depths measured in the central MOS1 detector have a median value of 41 ev, lower than the median of 56 ev of the traps identified through the analysis of the XRT corner source dataset as measured in the temperature range of 65 C to 55 C. The difference is significant, as the probability of the trap losses measured in XMM and XRT being from populations with the same distribution is very low, P= , as derived by applying the Kolmogorov-Smirnov test, that returns a D-statistic value of D= A direct comparison of the damage on board XMM-Newton and Swift would be at this stage misleading, the satellites being in very different orbits and radiation environments, but the measured lower average XMM charge losses could be the result of the colder MOS detectors and the freezing of a fraction of the trapping sites. Measurement of the ionisation energy The technique of minimisation of the modified χ 2 function developed for the analysis of the XRT corner source data was again applied in the MOS1 case with the goal of measuring the value of the ω parameter. Three sets of trap depth measurements have been investigated: the 2005 trap sample, the 2011 sample, and their combination. The 2005 sample includes most of the shallower defects detected in the MOS1 central detector, that were more easily identified in 2005 thanks to the better Al reference line photon statistics. The traps are on average deeper in the 2011 dataset; this generally makes the determination

120 91 Figure 3.20: Distribution of trap energy losses measured in the MOS1 central CCD, from the analysis of 2005 and 2011 calibration source data. of the defect location precise to the pixel, but the 2011 sample is smaller and the depth uncertainties larger than in 2005 because of the worse statistics. The minimisation method did not provide a positive outcome using the 2005 and 2011 trap measurements, with very similar minima for multiple ω values in the investigated energy range. A better result, at least from an initial visual inspection, was produced by merging the two samples, as shown in Figure 3.21 alongside the ordered sequence of trap depth measurements. The formal ionisation value derived from the minimisation technique is ω min = 3.51 ev Figure 3.21: (Left panel) - χ 2 minimisation results from the combination of the 2005 and 2011 samples of trap measurements. (Right panel) - Ordered sequence of trap energy losses from the combined 2005 and 2011 samples. The horizontal red lines are spaced by ω min = 3.51 ev.

121 92 The significance of the results from the three samples were tested through numerical simulations, as done in the case of the XRT corner source analysis. Even for the combined sample, the correct input ω parameter was recovered within an interval of ±0.1 ev in just 13% of the simulation runs, not enough to guarantee the statistical significance of the detection. The simulations showed that for this sample size of 109 measurements, the uncertainties would have to be reduced by approximately 70% to recover the input value of ω within an interval of ±0.1 ev in at least 68% of the times. Additional attempts at measuringω weretriedbyonlyincludinginthecombined2005and2011sample the trap measurements with uncertainties respectively lower than 4 and 3.5 ev. The modified χ 2 function minimisation method, after applying the thresholds, was still not successful, as verified by repeating the numerical simulations on the filtered trap samples. Summary and future work The XMM feasibility study has revealed that the MOS1 camera, like the Swift- XRT CCD, has severely suffered from radiation damage, with the generation of localised defects that cause large energy losses during the charge readout in the affected pixels. The sample of 109 traps identified in the 2005 and Fe calibration datasets was used to attempt the measurement of the electron-hole pair creation energy in silicon, but no statistically significant value of ω was derived fromthe χ 2 minimisation method. The major limitation of the analysis is the low X-ray statistics in the calibration source emission lines, resulting in uncertainties in the measured trap losses that numerical simulations show as too large for a successful derivation of ω. The immediate next step in this study consists of completing the radiation damage analysis including all the detectors of the MOS1 and MOS2 cameras. This would likely result in a final sample consisting of more than a thousand trap depth measurements. From this database, strict thresholds on the uncertainties could be applied, yet still allowing a trap sample size sufficiently large to significantly measure the ionisation energy value. In most cases, the trap depths have been derived by merging data over periods of multiple years, raising a concern regarding the stability of the trap depth measurements over these extended time intervals. Factors such as small

122 93 deviations of the gain coefficient or the build-up of shallower, single-electron traps in pixels below the original defect, could introduce offsets in the measured emission line energy during the long integration times. A detailed analysis of these effects is therefore needed to estimate their magnitude and determine their importance as possible source of errors. The analysis of the full XMM dataset and the evaluation of all the possible source of errors in the trap depth measurements is a very large task, and it goes beyond the immediate goal of the feasibility study presented here. The complete analysis will be achieved in a separate work in the near future. 3.6 Summary The radioactive corner source data, used to monitor and derive the detector gain and charge transfer inefficiency, are also an important resource to investigate the effects of radiation on the XRT detector. The evolution of the number of pixels affected by damage and the distribution of the energy losses has been presented. The measurement of the temperature dependence of the charge losses in individual pixels of the corner source regions revealed the presence of various kind of traps of specific charge emission times and characteristic energy levels. The energy level of the traps in specific pixels was also derived by modelling the recovery in energy observed in a few of the columns affected by large traps. The measurement of the corner source event energy along the columns were used to place constraints on the size and location of the damage clusters. I presented the procedure developed to derive ω, the energy to generate an electron-hole pair in silicon, using charge loss measurements from the corner source data. This is a novel method to measure the ionisation energy, not reported before in the literature and presented for the first time in this thesis. The optimal selection of integration time and length, temperature, grade and data quality filtering to obtain a successful measurement was investigated and implemented. The best sample yielded a value of ω = 3.73 ev, but the result could not be statistically confirmed through simulations, as the errors in the energy loss measurements proved too large for many damaged pixels. A feasibility study was presented regarding the use of XMM 55 Fe calibration source

123 94 datasets to measure the trap charge losses in the MOS detectors, and derive the ionisation parameter. Defects were identified in the central CCD of the MOS1 camera, but as in the XRT case the uncertainties in the measurements were too large to determine ω. The XMM analysis can be refined by including trap measurements from all the MOS detectors and by precisely evaluating the systematic effects caused by gain instabilities and the build-up of CTI.

124 Chapter 4 Characterisation of charge traps in a proton-damaged CCD 4.1 Introduction This Chapter presents thestudy ofthedamageonane2vccd22 similar tothe CCDonboardtheX-raytelescope, thatwasexposedtwicetoabeamof10mev protons. The proton damage was investigated by uniformly illuminating the CCD at the University of Leicester Camera Test facility with X-rays of selected energies. The X-ray technique is a standard method to measure the Charge Transfer Efficiency (CTE) in CCDs to assess the average amount of charge lost in a pixel during a frame readout process. In this work, the X-ray technique is exploited to identify and localise damaged pixels that cause charge losses of 20 ev; this is achieved with long total exposures (approximately 8 hours) of the CCD to an X-ray source, in order to collect a sufficient number of X-rays in every pixel of the detector to evaluate its spectral response. The damage in single pixels is studied as a function of CCD temperature, over a range covering the typical XRT operational values. Exposures of the CCD to X-rays of fluorescence line energies from oxygen (524.9 ev) to iron (6404 ev) are used to investigate the energy dependence of the charge losses. The X-raysource flux was variedto evaluateits effect onthe measured damage. This thorough analysis highlights the non-uniformity of the proton damage on the detector, reveals the existence of a range of trap behaviours and allows the comparison of the proton damage with the radiation damage experienced on 95

125 96 board the XRT. The dependencies on temperature, energy and flux provide insights on the different population of traps in the damaged pixels based on properties as the electron trapping and emission times. The CCD irradiation procedure is summarised in Section 4.2. The test facility and the instrumentation are described in Section 4.3. The data acquisition and the procedure and the techniques developed to measure the energy losses in the damaged pixels is shown in Section 4.4. The study of the temperature, energy and flux dependencies are presented in Sections 4.6, 4.7 and 4.8. The emission time constant in the pixels with the largest charge losses is derived in Section 4.9. The properties of the manufacturing traps, dating before the proton irradiation of the CCD are illustrated in Section 4.10, and a summary is given in Section CCD irradiation The radiation hardness of the CCD on board the Swift X-ray telescope (XRT) was studied before launch by the XRT calibration team at Leicester University by irradiating several e2v CCD-22, the same type of detector on board the XRT, with protons at the AEA Technologies beam line at the Tandem Accelerator facility in Harwell, Didcot. The CCD22 used in this study is a batch 5 device (B5/7) that was exposed twice to a 10 MeV proton beam. The proton beam line used for the irradiation is illustrated in Figure 4.1. The protons are scattered by a series of foils to uniformly illuminate the sample region. The details of the experimental setup and the irradiations are described in Ambrosi et al. (2005). During the first exposure, with a dose of MeV protons cm 2 the camera was partially shielded by a 3 mm thick Aluminium shield, protecting approximately half of the device. After a preliminary assessment of the incurred damage, the CCD was exposed to a second dose of protons cm 2 over the entire detector, such that half of the CCD was exposed to a total dose of protons cm 2 (Figure 4.2). For comparison, the total 10 MeV equivalent proton dose in orbit for the Swift-XRT camera during one year of operation is estimated at protons cm 2 (Short, 2000).

126 97 Figure 4.1: Proton beam line at the tandem accelerator facility, from Ambrosi et al. (2005). Protons are scattered by the scattering foils to create a uniform irradiation of the CCD mounted on the sample plate. Figure 4.2: Irradiation doses on the CCD. The left-hand area of the detector was exposed to a dose MeV protons cm 2, the right-hand area was exposed twice, for a total dose of MeV protons cm 2.

127 98 Figure 4.3: The test facility where the proton damaged CCD, mounted within the cryostat and cooled to the selected temperature was illuminated with X- rays of different energies generated with the soft X-ray source and the high energy KEVEX source. 4.3 Instrumentation The analysis of the proton damaged Batch 5/7 CCD22 was performed at the Camera test facility of the Space Research Centre at the University of Leicester. At the facility, the CCD is cooled and kept at a controlled temperature using liquid nitrogen and can be uniformly illuminated at selectable soft X-ray energies by using an electron bombardment source with a copper anode and a tungsten filament or a KEVEX high energy emission source (Figure 4.3 is a photograph of the testing facility showing the KEVEX tube and the cryostat). Three vacuum chambers contain the soft source, the high energy source and the pumping system and the detector chamber where the camera is placed. The copper anode of the low energy X-ray source is oil cooled and coated with different compounds to generate specific emission lines selected by using a Bragg crystal monochromator, from the boron Kα line at 185 ev to the chlorine Kα line at 2.6 kev. Five crystals can be mounted on a pentagonal mounting block, and a rotary drive is used to select the appropriate angle of incidence according to Bragg s law,

128 99 nλ = 2dsinθ (4.1) wherenisanintegernumber, λisthewavelength oftheincomingphotons, θ is the radiation incident angle with the plane of the crystal and d is the spacing between the atomic planes in the crystal structure. For our analysis, we utilised oxygen emission Kα at ev and copper Lα X-rays at ev. Oxygen X- rays only partially illuminate the imaging area of the CCD, so halfway through the exposure the crystal was slightly rotated to achieve full illumination of the camera. The beam of the KEVEX source illuminates metals on a rotating target wheel to select a range of fluorescence emission lines, from aluminium to gallium arsenide. In our experiment, the angle of the rotary drive was set to generate silicon (1.740 kev), titanium (4.510 kev) and iron (6.404 kev) Kα lines. The CCD is placed in the cryostat, in contact with a cold finger cooled with liquid nitrogen to remove heat from the system with an evaporative process. A Platinum Resistance Thermometer (PRT) junction, in contact with the CCD clamping mechanism, is used to monitor the system temperature. A temperature controller continuously maintains the temperature at the set value adjusting the current of the resistors attached to the clamps. 4.4 Data processing and analysis All the data used in the radiation damage analysis were acquired and recorded at the Swift test facility following the procedure described here. Firstly, the CCD driver circuitry and the device are turned on and the camera is cooled at a set temperature. The driver circuitry, based on XMM-EPIC electronics, is connected to an Acorn Computers RISC PC. Software developed by the XMM- EPIC and the Swift-XRT teams is installed on the Acorn PC and is used to drive the CCD by setting the acquisition and operational parameters, and to record the data collected during the experiment. Before the X-ray source is turned on, 10 image frames are recorded to measure the bias level, estimated as the median value of each pixel in the images. The bias map thus created is subsequently subtracted directly by the Acorn recording software while the frames are collected. Prior to the exposure

129 100 with the selected X-ray source, the CCD gain is estimated by collecting a limited number of frames(typically five) using the reference kev titanium Kα X-rays from the KEVEX source, and the bias map subtraction turned on. The cumulative histogram of the energy of the detected X-rays is displayed and the line peak of the titanium Kα X-rays is fitted with a Gaussian function by the acquisition software. The gain coefficient thus derived is stored in the files header. The sample source energy is subsequently selected and a few test framesaretakentoevaluateandadjustthex-rayfluxandtoensuretheuniform illumination of the detector. Finally, the number of frames to be recorded is set by the acquisition software and the data collection takes place. Typically, ten thousand frames were collected for each energy and temperature setting in approximately 8 hours, at a flux of 600 X-rays per frame, for an average of 1 event per column per frame. The frame exposure time is 2.5 s, as on board the Swift XRT camera. A shorter total collection time using higher source fluxes was not possible, as the detection of 2 or more X-rays per column in a single frame would compromise the traps analysis: in such an instance, the first X-ray event to be read out would fill the trap, while the charge of the second X-ray would be transferred through the damaged pixel unaffected. The datasets are recorded by the Acorn software as 5-byte format files for storage efficiency. A modified version of the IDL code developed by the research group at the Camera facility is used to read the data, extracting values from the file header relative to the CCD driving configuration such as voltages, temperatures, imaged region, readout time and number of frames, and saving the information on the detected X-rays(detector coordinates, energy and grade) in FITS file format. IDL code developed for the analysis of the Swift XRT trap mapping calibration observations has been adapted for the laboratory datasets to remove hot pixels and extract X-ray events for each CCD column. The X-ray emission line is fitted along the columns, merging events from a fixed number of adjacent pixels, using a Gaussian plus constant function. Ten pixels binning were found to provide sufficient statistics for a reliable fit and were generally used in the analysis. As in the case of the XRT analysis, the IDL MPFIT package developed by Craig Markwardt was used to perform the curve fitting. MPFIT is more robust than the IDL built-in curvefit package, and also allows the use of the appropriate statistics (Cash, 1979) for the low number of X-ray events.

130 101 The energies along the CCD columns derived by the fits to the emission line are used to identify damaged pixels where charge is trapped. With an average of X-ray events detected per pixel, charge losses larger than approximately 20 ev can be found. The precise location of the damaged pixel is derived iteratively by merging pixels above and below the proximity of the preliminary damage position and finding the maximum energy difference; mergings of 5, 10 and 20 pixels are used, and the fits are manually inspected. The location refinement is performed at first using the titanium dataset CCD temperature of 75 C, for which the traps are deepest, and subsequently checked in datasets at other temperatures and energies. Once the damaged pixel has been identified, its trap depth is measured by fitting a set of functions to the emission line energies in the pixels below and above the damage. The functions used in the fits are the linear, quadratic, geometric (Y = a0 x a1 +a2) andlogsquarefunctions (Y = a0+a1 lgx+a2 lgx 2 ). The majority of energy profiles were well fitted by either the linear or the quadratic functions, while in just a few cases the geometric or the logsqure functions provided statistically better fits. The measured damage depth is the difference of the best fits in the damaged pixel and the pixel just below it. An example is given in Figure 4.4, for the damaged pixel (16,160). Almost one hundred damaged pixels were found (out of a total of 600x600 detector pixels) with a trap depth of 20 ev or more, as measured using the titanium Kα X-ray source at the CCD temperature of 75 C. The distribution of measured trap depths is shown in Figure Pre-irradiation dataset Charge traps in a CCD can be caused by several mechanisms other than displacement damage caused by particle irradiation. In particular, manufacturing defects can cause localised potential barriers that lead to the trapping of charge during the readout process. To properly identify the nature of the traps detected in the radiation damaged CCD22 we characterised the response of the camera before it was exposed to protons at the Tandem Accelerator facility, by analysing a set of reference, pre-irradiation datasets collected in October The richest of these dataset consisted of 2000 frames yielding 1.5 mil-

131 102 Figure 4.4: Measurement of the charge losses in the damaged pixel (16,160) in thetitaniumdataset at 75 C. Themeasured energies ofthetitaniumkα line above and below the damaged pixel are fitted with two quadratic functions and the trap depth is calculated as the difference of the fit values in the damaged pixel and the pixel just below it. The decline in the slope above (16,160) is likely caused by additional charge losses in pixels above the large trap. Figure 4.5: Distribution of trap depths measured using titanium Kα X-rays at the CCD temperature of 75 C. lion titanium Kα X-ray events at 4.5 kev, corresponding to a flux of 1 X- ray/column/frame, similar to the datasets collected for the proton damage

132 103 Figure 4.6: Deep manufacturing defect in column 134, evidenced from the analysis of a pre-irradiation dataset. The X-ray events detected in 20 adjacent pixels weremerged, andthemeasured energyofthetitaniumkα linewasfitted by a Gaussian plus a constant function. In the figure, the energy at coordinate DETY is derived from merging X-ray photons detected in pixels [DETY-19, DETY]. analysis. The relatively good statistics allowed the use of the same code developed for the proton damage study (with modifications to account for the different storage format in use at that time) to reduce and inspect the data. The titanium emission line was fitted along the columns with a Gaussian plus a constant model, and energy profiles were extracted to allow the identification of charge trapping sites. The analysis revealed that the deepest traps ( 200 ev) were already present in 1998, before the proton exposure (see for example Figure 4.6). The very deep traps in columns DETY = 132, 133, 134, 135, 295, 317, 355, 360, 411, 551 and 555) are therefore manufacturing defects. On the other hand, traps with a depth of the order of ev (as in columns 99, 159, 251, 323, 593) are not seen in pre-irradiation data, and were therefore caused by protons.

133 Temperature dependence The characterisation of the temperature dependence of the charge traps is critical to achieve the Swift-XRT best possible spectral resolution. The XRT camera, after the failure of the power supply of the thermoelectric cooler(tec) shortly after launch, is operated at a temperature between 75 C and 45 C, depending on the spacecraft orientation during flight. The main consequences of the higher than expected temperature have been an elevated electronic signal and the increase of charge transfer inefficiency that worsened the spectral resolution. The Shockley-Read-Hall theory (Shockley & Read, 1952) describes the process of capture and release of carriers by traps. Charge is trapped with a capture time scale τ c (Equation 1.11). As an example, the Phosphorous-Vacancy (P-V) defect, that is likely the main cause of the CTE degradation on board the XMM-Newton MOS CCD (Lumb, 2004), with a capture cross section of cm 2 has a capture time of less then 0.1 µs. As the parallel transfer time of a charge through a pixel in the XRT CCD takes 15 µs the trapping process of the P-V defect can be considered instantaneous. The trappedcharge isthenreleased withanemission time scale τ e thatisan exponential function of the CCD temperature and the trap energy E t (Equation 1.12). The emission times are orders of magnitude longer than the capture times (see Table 1.1 and Figure 1.11). At very low temperatures (T 100 C) most traps are effectively frozen out : once a trap is filled, the charge is released on such long timescales that the subsequent detected X-rays are not affected by the damaged pixels during the transfer and readout processes. At high temperatures, on the other hand, the elevated dark current signal can fill the traps during a frame exposure, so that less charge is lost from the X-ray charge packet during the transfers. As reported in Chapter 3, the analysis of the XRT calibration datasets has revealed a general trend of reduced charge losses at higher CCD temperatures but also diverse temperature dependence behaviours in individual defects detected in the corner source study. The analysis of the proton-generated damage aims at discovering whether different trap populations can be detected and to derive constraints on their characteristic energy levels, to compare their properties with the traps generated by the space radiation environment on board

134 105 the XRT Data samples and results Two sets of data have been taken at the test facility for the temperature dependence study. The first sample consists of datasets of titanium Kα X-rays at 4.5 kev, at a flux of approximately 600 single pixel events per frame. The CCD temperature was varied in steps of 5 C between 50 C and 75 C, to reflect the operational range of the XRT camera. An additional dataset was acquired at 100 C to probe the cold regimes where traps can freeze. The second set of data was taken using silicon as the X-ray source (Kα X-rays of 1.7 kev) at temperatures of 50 C, 60 C and 75 C. This was done to investigate possible secondary energy dependence effects on the the trap s temperature properties. The CTI is clearly worse at all temperatures in the region of the CCD exposed to a higher proton dose. Because of this, the identification of traps is easier in the less damaged section, while the shallower traps can become blurred in the general CTI losses in the area of higher dose, in particular at higher temperatures. The majority of damaged pixels showed the deepest charge losses at 70 C or 75 C. Table 4.1 includes all the traps identified in the titanium datasets with charge losses greater than 20 ev in at least one of the temperature setting, and their depths at all the investigated temperatures. The trap depths were calculated as described in Section 4.4; the uncertainties are derived from the standard errors of the fits to the line energies at the damaged pixel position and at the pixel just below it. In Figure 4.7 the spatial distribution of the defects is shown. The shallower traps, of charge losses of less than 50 ev at 75 C, plotted as black stars, are mostly in the region of CCD exposed to the lower dose, while the region on the right, exposed to twice the initial dose presents a higher density of the more damaged pixels (red stars), with charge losses greater than 50 ev; the plot also shows the pixels with manufacturing defects (blue triangles).

135 106 Table 4.1: Traps depths (in ev) at fixed CCD operational temperatures between 100 C and 50 C from titanium Kα (4.510 kev) fluorescence line energy datasets. When no significant trap depth was measured a blank entry was left in the table. Manufacturing defects are reported at the end of the table, starting with damaged pixel (132,104). (X,Y) 50 C 55 C 60 C 65 C 70 C 75 C 100 C (16,160) 45±5 37±3 54±5 34±3 57±3 47±6 54±3 (18,305) 20±5 20±3 11±3 27±6 12±3 (22,278) 13±3 17±5 22±5 48±8 21±11 10±6 (33,174) 39±19 27±15 30±11 (42,175) 12±7 16±6 28±7 35±5 65±8 38±3 (43,528) 22±6 41±5 19±7 21±6 37±5 16±8 14±10 (45,393) 22±6 35±5 23±5 25±3 17±3 20±8 (46,174) 24±15 37±18 22±18 43±8 37±19 (55,532) 28±14 17±16 25±17 36±16 44±9 58±18 (58,174) 35±20 39±12 30±11 27±16 39±15 19±8 29±13 (66,252) 11±7 23±4 42±5 46±7 42±5 31±9 44±7 (66,322) 48±7 48±7 48±10 28±5 45±13 27±7 (69,175) 15±6 20±7 28±7 27±5 16±8 29±12 (74,166) 27±7 16±4 22±5 17±8 (85,167) 11±5 26±4 21±6 18±6 19±5 35±9 (86,174) 31±17 32±21 45±13 23±17 28±17 41±13 36±17 (94,481) 33±4 28±7 29±4 36±9 46±11 52±5 28±6 (96,361) 35±21 33±32 50±30 55±20 (97,114) 14±4 23±3 33±5 17±3 30±3 41±3 21±2 (99,293) 65±4 59±5 66±6 51±3 91±10 66±16 78±6 (115,290) 27±3 41±5 40±3 20±5 38±4 41±3 (119,293) 30±4 41±5 50±3 62±5 41±4 74±8 54±6 (136,128) 26±5 18±3 (142,131) 35±15 41±12 18±14 25±14 22±11 32±13 (145,451) 22±2 18±6 40±5 17±3 16±7 44±6 33±5 (146,132) 28±20 42±17 35±8 48±17 Continued on next page

136 107 Table 4.1 continued from previous page (X,Y) 50 C 55 C 60 C 65 C 70 C 75 C 100 C (159,400) 20±6 40±5 78±8 70±8 90±5 95±6 (169,132) 25±10 24±20 20±19 52±10 30±16 (178,141) 29±4 40±4 38±5 33±4 36±3 25±11 47±6 (184,351) 27±13 20±16 25±10 (187,133) 18±4 18±4 19±4 45±3 35±5 72±3 (193,554) 46±2 56±4 66±4 64±2 80±3 76±5 55±3 (194,510) 40±4 25±7 26±4 59±4 63±5 47±7 38±3 (204,133) 32±16 21±12 18±17 38±17 46±10 54±16 (214,210) 32±16 35±17 19±12 32±12 24±10 23±14 (216,330) 24±4 13±4 17±5 (217,233) 25±6 22±4 37±4 37±6 11±7 12±3 (223,279) 21±4 11±5 21±15 (225,496) 17±5 25±4 34±4 62±5 61±5 83±6 85±4 (228,313) 32±6 21±11 (229,499) 26±4 11±7 22±6 16±6 22±8 (236,491) 48±5 46±3 41±4 36±4 44±5 54±7 52±4 (239,103) 27±18 30±14 46±16 45±13 (249,501) 15±4 25±4 47±6 42±6 48±6 34±11 20±9 (251,332) 57±10 45±7 60±7 67±7 61±5 114±11 53±10 (253,175) 34±24 32±18 31±9 36±20 39±12 30±12 39±23 (258,280) 29±6 31±21 (260,203) 44±10 31±7 32±4 39±6 52±8 88±22 47±7 (261,280) 64±8 65±9 88±10 93±4 80±6 97±7 86±8 (262,463) 40±13 45±19 26±16 47±12 54±28 (262,550) 87±9 62±6 77±5 45±7 86±6 76±16 62±10 (263,344) 13±6 19±5 29±3 39±10 18±7 (267,223) 25±8 42±6 50±9 44±8 32±8 35±17 (267,483) 84±10 51±6 41±10 98±10 84±5 100±27 100±16 (273,327) 81±11 71±18 52±17 64±24 69±20 53±13 40±26 (275,116) 15±9 44±6 16±10 26±12 (291,542) 53±10 49±10 56±11 55±12 37±13 46±15 45±9 (292,202) 42±6 34±9 74±8 62±8 55±6 72±13 49±10 (293,438) 35±5 56±5 56±4 74±4 76±6 100±6 68±6 (301,419) 28±17 24±19 26±14 31±10 Continued on next page

137 108 Table 4.1 continued from previous page (X,Y) 50 C 55 C 60 C 65 C 70 C 75 C 100 C (303,110) 20±5 18±6 60±9 44±13 (303,523) 54±4 29±5 43±5 34±2 66±3 115±11 49±9 (304,160) 18±7 66±7 62±6 77±6 77±7 90±3 29±9 (319,112) 44±13 37±6 46±5 68±8 83±8 36±9 37±6 (319,399) 27±8 20±13 59±12 58±12 53±12 48±4 32±13 (322,91) 72±10 71±13 40±6 47±15 55±12 38±10 59±17 (323,381) 41±5 95±5 124±6 114±13 119±8 136±27 104±9 (325,328) 32±6 27±6 43±5 76±5 78±6 53±7 97±8 (332,226) 61±3 51±2 31±5 42±4 36±2 57±31 26±5 (335,576) 48±3 57±5 65±3 99±8 97±4 35±31 97±7 (339,240) 36±5 14±5 31±5 31±9 41±3 77±10 59±10 (342,279) 21±11 80±7 44±4 35±7 76±5 33±13 43±7 (345,180) 39±8 12±8 15±10 35±7 40±11 68±23 21±6 (346,535) 24±18 53±22 69±22 39±21 46±12 (348,415) 51±5 49±4 64±5 93±5 112±7 48±10 61±8 (359,498) 54±11 43±7 80±4 91±11 98±9 81±20 114±14 (375,411) 71±13 55±12 (378,544) 57±4 62±7 33±6 40±5 60±7 54±8 15±3 (387,438) 19±5 18±6 42±4 13±10 (398,98) 19±17 35±16 25±15 49±20 41±23 25±15 60±26 (403,496) 53±10 83±13 61±8 70±9 55±15 81±14 40±17 (408,221) 79±9 86±3 84±5 67±6 48±30 30±21 34±10 (410,159) 14±9 29±5 24±6 31±5 76±4 135±25 132±7 (411,406) 14±4 58±5 43±5 52±4 75±5 86±10 52±15 (421,354) 64±13 94±8 89±17 82±7 94±11 120±11 78±9 (431,161) 21±7 43±9 60±7 45±6 39±6 41±12 72±8 (435,163) 32±20 44±17 48±11 56±26 38±26 57±16 (448,394) 12±3 27±4 40±3 43±2 71±6 41±8 (450,77) 60±5 24±4 41±4 46±5 53±4 81±40 29±6 (450,528) 40±9 17±8 44±8 55±7 73±8 55±33 80±7 (454,332) 44±8 72±13 65±7 69±14 74±8 52±21 16±5 (457,178) 31±14 36±20 42±19 64±13 65±34 (460,57) 41±20 37±19 47±15 72±23 70±27 91±14 53±27 (461,72) 36±8 49±5 70±5 103±6 105±7 151±21 53±11 Continued on next page

138 109 Table 4.1 continued from previous page (X,Y) 50 C 55 C 60 C 65 C 70 C 75 C 100 C (462,265) 30±13 37±6 68±7 49±7 65±9 53±13 (470,203) 90±5 84±6 70±7 76±8 76±12 107±14 49±10 (481,534) 34±8 69±14 45±12 27±7 39±12 71±15 (484,440) 28±22 28±20 56±10 33±19 34±17 39±10 25±23 (485,195) 64±3 69±6 51±4 76±3 83±3 12±9 32±7 (487,369) 67±7 61±5 78±7 46±6 56±5 62±7 50±11 (494,336) 10±3 24±6 32±5 59±4 30±4 51±8 51±6 (494,468) 33±7 84±6 70±12 75±11 72±6 71±26 76±16 (501,116) 62±5 77±7 71±5 82±5 95±7 97±38 74±10 (501,372) 44±4 73±4 55±4 71±3 64±3 32±12 30±6 (502,521) 29±22 40±20 33±10 19±18 46±21 50±17 34±28 (503,247) 77±20 75±18 52±16 46±23 43±21 54±16 53±30 (514,342) 31±7 51±6 73±12 80±11 35±23 31±7 (518,184) 42±21 38±21 40±14 41±18 61±19 65±15 46±29 (529,150) 38±25 32±14 54±17 65±29 69±23 65±18 (541,257) 77±17 56±16 53±11 80±27 82±29 60±13 (541,451) 47±7 78±9 99±13 109±10 123±14 99±32 83±15 (548,339) 37±17 56±25 58±16 40±24 28±21 62±13 (549,81) 43±19 37±19 57±14 58±20 62±21 49±12 (550,50) 45±28 57±22 48±23 48±26 49±13 66±17 42±29 (559,98) 48±19 61±11 80±8 86±15 114±11 124±24 130±12 (560,345) 41±6 46±6 37±6 73±8 106±33 61±7 (560,484) 51±7 44±7 49±5 73±5 75±7 75±23 68±4 (569,275) 25±19 21±19 50±16 31±21 60±21 44±16 29±26 (573,441) 43±25 78±15 58±12 69±29 41±29 66±17 66±28 (593,113) 78±13 123±15 84±9 125±9 132±15 136±17 85±10 (596,277) 20±8 32±4 49±7 71±7 40±8 (132,104) 149±13 227±9 317±13 360±14 393±13 426±15 426±15 (133,255) 127±13 188±10 236±10 277±13 283±12 283±27 311±15 (134,407) 228±7 295±15 306±16 303±14 345±10 370±29 338±11 (135,558) 193±14 224±11 306±14 350±12 392±7 484±29 509±13 (295,376) 102±6 185±7 652±16 922± ±7 1185± ±4 (317,114) 239±11 277±13 343±2 393±3 428±5 457±19 360±10 (355,172) 91±5 111±3 118±6 183±5 195±3 252±6 140±5 Continued on next page

139 110 Figure 4.7: Map of the deep defects, in detector coordinates. Black stars - Pixels with charge losses E 50 ev at 75 C, identified in preference in the left area of the detector, exposed to the lower proton dose; Red stars - Pixels with charge losses E > 50 ev, mostly present in the right CCD region of higher proton dose; Blue triangles - Manufacturing defects. Table 4.1 continued from previous page (X,Y) 50 C 55 C 60 C 65 C 70 C 75 C 100 C (360,179) 153±9 190±7 253±6 279±4 305±5 331±12 290±7 (411,20) 155±7 174±12 211±19 242±16 269±10 278±15 264±14 (551,312) 114±11 155±6 199±5 225±5 266±6 318±6 284±4 (555,465) 288±20 307±18 406±13 477±18 434±10 463±25 436±26 Two main competing factors can introduce a dependence on the temperature of the trap depths, the thermal background signal and the emission time scale. Radiation damaged pixels are both charge trappers as well as generators

140 111 of thermal electrons (leakage current, Srour & McGarrity (1988)). At warmer CCD temperatures the dark current signal increases, and can fill a high number of trap energy levels. On the other hand, the charge release time increases at colder temperatures, and the trap energy levels can remain occupied on a timescale longer than a frame integration and transfer time, resulting in a decrease of the effective depth of the defect. We find two main classes of trap behaviour: a small group of damaged pixels shows no dependence on temperature of the amount of lost charge; in the second the charge loss is higher as the temperature is lowered, but in most cases the trend is slowed or even reversed at the coldest CCD operational settings. In the first group of damaged pixels, as in the case of pixels (86,174) and (291,542), shown in Figure 4.8, there is a flat dependence of the trap depth over the temperature range under investigation. Two factors could explain a flat dependence, a very quick emission timescale or a low dark current. In these pixels, the thermally generated charge accumulated during the exposure time could be very quickly emitted as the readout process starts, resulting in the same number of empty trap levels in the damaged pixel for all the X-ray events at all the tested temperatures. The very quick detrapping time can be tested by investigating the sacrificial charge effect (see Section 4.8), that can be seen when two (or more) X-rays are transferred over the same trap during a single frame readout. The sacrificial charge effect is indeed seen in this set of traps, therefore excluding a very short emission time. Alternatively, if the thermal electronic signal is too low in these damaged pixels to significantly fill the traps the charge losses won t be less at higher temperatures. The low dark current interpretation is supported by the fact that the flat dependence is only seen in traps with depth of 50 ev or less, while in the most damaged pixels that likely generate higher background signal the charge losses increase as the temperature is lowered. In the second set of pixels, that constitutes the large majority of detected defects, the charge loss is generally higher as the temperature decreases, with subgroups that can be identified based on the shape of the dependence; while a small number of pixels presents a linear trend, in most pixels there is a curved dependence that in the extreme cases can turn to a decrease in charge losses at the coldest temperatures (Figure 4.9). In pixels presenting a linear temperature dependence, thereisnoevidence fora freezing ofthetrapseven atthecoldest

141 112 Figure 4.8: A set of damaged pixels that presents no change in charge losses at different CCD operational temperatures. Examples shown here are from pixels (262,550) and (291,542). temperatures; as no decrease in charge loss is seen, the charge emission time scale must be shorter than the average frame exposure and transfer time even at 100 C. As can be derived from Equation 1.12, a detrapping time shorter than several seconds at 100 Cis indicative ofcharacteristic trapenergy levels below 0.4 ev. In these damaged pixels the dark current signal is likely the sole responsible factor in determining the functional dependence. The curved dependence scenario is more complex, as two likely effects can each explain the observed behaviour. In some pixels, saturation of the trapping sites could have been reached, as the transfer of the X-rays fills the entire damage cluster; in other pixels, the increase of the emission time scale at the lower temperatures could partially fill the traps during a frame readout. The investigation of the silicon datasets can help to distinguish between the two hypotheses, as the lower energy 1.7 kev silicon Kα X-rays generate fewer electrons and a smaller charge cloud when photo-absorbed with respect to 4.5 kev titanium Kα photons. The silicon sample is limited to just three datasets at temperatures of 50 C, 60 C and 75 C; nevertheless, the temperature baseline was sufficient to confirm the curved dependence for a set of pixels, with the flattening below T 60 C, for which the release time in some of the trap energy levels becomes larger than the average X-ray parallel readout time (of the order of 5 milliseconds if the charge is transferred over half the rows of the detector). From Equation 1.12, such timescales below T 60 C imply trap energy levels of E t 0.35 ev.

142 113 Figure 4.9: Examples of damaged pixels with a continuous increasing depth at colder temperatures (pixel (410,159), top panel), defects with a flattening of the charge losses below 75 C (pixel (225,496), central panel), and a drop in trapped charge at the highest temperatures (pixel (541,451), bottom panel).

143 114 The effects of the long charge detrapping time is even more pronounced in pixels that present a shallower depth at the coldest temperatures, for which a fraction of the traps in the damaged pixel remains filled on timescales longer than the frame readout time. In these pixels, shallow energy levels of the frozen traps can be excluded, as their characteristic detrapping time stays below one second even at the coldest temperatures. Therefore, from Equation 1.12, a significant fraction of these defects presents trap energy levels of 0.4 ev or larger. The results of the analysis of proton-generated defects are consistent with what has been found on board the XRT. While a variety of temperature dependencies is observed in single pixels, the large majority of traps identified in the proton irradiated CCD presents the general trend seen in the analysis of the Tycho trap mapping observations, the instrumental nickel line and the corner source datasets, with reduced charge losses at the warmer temperatures likely caused by an elevated background current signal and the freezing effect kicking in at the colder settings. The observed behaviour implies that most defects are characterised by trap energy levels E t of approximately 0.35 ev or larger. 4.7 Energy dependence The number of electrons generated by the absorption of an X-ray photon in the CCD is proportional to the incident photon energy through the ionisation parameter ω (Equation 1.1), approximately equal to 3.63 ev at room temperature (Owens et al., 1996). During the readout process, the signal electrons are confined in the potential well under the electrodes of the pixels. The physical size of the signal cloud is generally assumed a function of the number of electrons, so that a stronger signal occupies a larger volume in the pixel. In volume-driven CTI models, the number of traps interacting with the charge cloud is considered dependent on the signal volume; based on this assumption, the study of the energy dependence of the traps could therefore provide information such as the size of the defect in the damaged pixels and help differentiate between different trap populations. In the case of the e2v CCD22 instead, the cloud volume is considered independent of the signal size for charge packets below 2000 electrons, and

144 115 is increased only by larger signals that can alter the shape of the electrode potential (see Holland (1993), that characterised the CTI in irradiated CCD22 devices). In this scenario, the signal cloud encounters all the traps in the pixel but the number of trapped electrons depends on the packet density; the measured energy dependence of the trap depths can therefore be interpreted in terms of the stochastic process of capture and release of the electrons by the defects, with signals of higher electron density losing more charge during transfers. A CTI dependence on electron density rather than volume for small charge packets has also been observed in recent studies aimed at modelling the radiation-induced charge losses in Gaia CCDs (Short et al., 2013). The density-driven model will be used in this work to interpret the results of my investigation. We studied the trap energy dependence in the damaged pixels by uniformly illuminating the proton irradiated CCD with X-rays at the fluorescence lines of oxygen (524.9 ev), copper (929.7 ev), silicon (1.740 kev), titanium (4.510 kev) andiron(6.404kev). TheCCDtemperaturewasset at 75 C, where thetraps appear deeper. Ten thousand frames were collected in each case, with the bias subtraction turned on and calibrating the gain using the titanium source as described earlier. The measured trap depths at the selected fluorescence line energies are reported in Table 4.2. Table 4.2: Traps depths (in ev) measured at the oxygen Kα (524.9 ev), copper Lα (929.7 ev), silicon Kα (1.740 kev), titanium Kα (4.510 kev) and iron Kα (6.404 kev) fluorescence line energies at 75 C. Ablank entry is entered when the trap depth could not be confidently measured. Manufacturing defects are reported at the end of the table, starting with damaged pixel (132,104). (X,Y) O Cu-L Si Ti Fe (16,160) 17±4 26±2 38±3 47±6 57±2 (18,305) 16±5 11±4 27±6 19±3 (22,278) 18±2 20±3 21±11 19±5 Continued on next page

145 116 Table 4.2 continued from previous page (X,Y) O Cu-L Si Ti Fe (33,174) 25±15 13±11 30±11 40±20 (42,175) 23±2 23±9 25±4 65±8 38±6 (43,528) 16±8 25±6 (45,393) 14±1 20±8 20±4 (46,174) 18±12 43±8 33±14 (55,532) 24±17 17±13 44±9 30±21 (58,174) 18±8 19±8 (66,252) 11±3 17±4 31±4 31±9 38±4 (66,322) 28±6 45±13 38±9 (69,175) 14±3 18±4 16±8 28±4 (74,166) 10±5 17±8 13±7 (85,167) 10±5 35±9 16±7 (86,174) 16±14 41±13 32±15 (94,481) 10±2 52±5 54±21 (96,361) 24±23 36±21 55±20 27±26 (97,114) 19±1 21±10 41±3 43±9 (99,293) 54±12 59±8 68±10 66±16 83±10 (115,290) 39±3 38±2 38±4 50±3 (119,293) 22±2 74±8 65±2 (136,128) 13±1 18±3 19±4 (142,131) 11±6 29±7 22±11 16±12 (145,451) 37±8 28±7 50±8 44±6 (146,132) 19±11 35±14 29±12 35±8 38±18 (159,400) 45±4 65±6 86±4 95±6 115±8 (169,132) 23±9 12±12 33±12 52±10 18±17 (178,141) 25±11 34±3 (184,351) 8±7 25±10 (187,133) 23±3 35±5 28±4 (193,554) 13±2 34±2 76±5 64±3 (194,510) 14±2 18±3 47±7 59±4 (204,133) 23±9 37±12 29±10 46±10 48±17 (214,210) 14±11 32±13 24±10 30±19 Continued on next page

146 117 Table 4.2 continued from previous page (X,Y) O Cu-L Si Ti Fe (216,330) 22±2 17±5 31±4 (217,233) 13±2 31±4 14±2 11±7 46±3 (223,279) 11±2 22±2 21±15 24±4 (225,496) 53±4 60±4 58±6 83±6 87±9 (228,313) 12±3 21±11 14±11 (229,499) 16±6 39±5 (236,491) 54±7 54±4 (239,103) 31±14 45±13 24±20 (249,501) 31±2 34±11 51±5 (251,332) 30±3 33±4 78±4 114±11 102±7 (253,175) 30±12 31±19 (258,280) 31±21 (260,203) 88±22 59±12 (261,280) 14±2 20±1 97±7 131±6 (262,463) 37±19 36±14 47±12 27±22 (262,550) 16±11 45±9 76±16 69±13 (263,344) 23±6 39±10 47±5 (267,223) 32±8 63±6 (267,483) 15±8 41±8 100±27 86±10 (273,327) 53±13 28±27 (275,116) 23±6 35±4 16±10 20±6 (291,542) 19±7 46±15 89±33 (292,202) 45±7 56±5 82±6 72±13 57±18 (293,438) 27±1 46±8 59±3 100±6 108±7 (301,419) 17±13 16±12 31±10 47±22 (303,110) 54±3 23±8 70±7 44±13 49±5 (303,523) 42±6 75±14 76±5 115±11 104±9 (304,160) 13±3 48±4 90±3 91±5 (319,112) 49±4 50±6 42±3 36±9 54±5 (319,399) 36±2 48±4 22±4 (322,91) 35±3 39±7 50±4 38±10 49±10 (323,381) 26±5 41±22 82±11 136±27 101±9 Continued on next page

147 118 Table 4.2 continued from previous page (X,Y) O Cu-L Si Ti Fe (325,328) 93±6 50±6 81±3 53±7 61±19 (332,226) 19±4 57±31 74±9 (335,576) 80±4 94±10 70±12 35±31 110±18 (339,240) 21±10 22±4 77±10 51±5 (342,279) 19±5 37±4 22±4 33±13 57±6 (345,180) 68±23 67±11 (346,535) 15±9 26±20 23±13 46±12 39±26 (348,415) 26±3 48±13 64±10 48±10 56±17 (359,498) 24±6 49±5 77±6 81±20 107±11 (375,411) 55±12 (378,544) 54±8 71±4 (387,438) 13±1 26±5 11±4 13±10 73±5 (398,98) 18±11 25±15 44±23 (403,496) 43±4 42±7 59±5 81±14 85±9 (408,221) 43±14 30±21 (410,159) 10±4 82±6 94±13 135±25 124±18 (411,406) 32±5 37±16 47±7 86±10 69±12 (421,354) 38±4 40±4 83±6 120±11 115±9 (431,161) 46±14 44±13 58±9 41±12 57±26 (435,163) 17±13 57±16 76±19 (448,394) 31±8 42±11 28±20 71±6 84±25 (450,77) 22±6 25±16 35±11 81±40 55±17 (450,528) 27±9 40±21 44±12 55±33 52±24 (454,332) 20±7 41±13 38±10 52±21 53±14 (457,178) 42±11 30±16 31±15 64±13 43±26 (460,57) 47±14 91±14 81±32 (461,72) 29±5 31±11 52±5 151±21 107±17 (462,265) 20±4 35±15 46±6 65±9 80±6 (470,203) 15±7 107±14 94±37 (481,534) 28±4 42±6 39±12 65±18 (484,440) 20±10 39±10 34±23 (485,195) 35±10 36±3 12±9 57±3 Continued on next page

148 119 Table 4.2 continued from previous page (X,Y) O Cu-L Si Ti Fe (487,369) 62±7 75±4 (494,336) 13±5 17±4 21±4 51±8 54±6 (494,468) 27±8 42±17 84±8 71±26 72±24 (501,116) 33±8 69±7 53±9 97±38 54±12 (501,372) 10±1 32±12 97±6 (502,521) 18±14 26±16 50±17 59±27 (503,247) 20±12 54±16 56±25 (514,342) 21±6 30±6 35±23 77±13 (518,184) 25±17 38±13 65±15 57±23 (529,150) 21±17 31±16 65±18 63±28 (541,257) 38±17 60±13 80±23 (541,451) 31±7 72±15 59±12 99±32 89±9 (548,339) 35±15 62±13 35±24 (549,81) 49±12 25±23 (550,50) 9±8 15±14 17±15 66±17 81±24 (559,98) 83±12 75±12 90±12 124±24 109±14 (560,345) 14±13 32±14 106±33 74±31 (560,484) 49±13 61±13 60±7 75±23 107±30 (569,275) 37±15 44±16 40±24 (573,441) 22±18 33±13 66±17 50±23 (593,113) 57±10 87±15 81±5 136±17 133±19 (596,277) 71±7 48±19 (132,104) 253±4 325±5 357±11 426±15 479±14 (133,255) 125±4 200±13 211±6 283±27 312±20 (134,407) 156±4 190±6 260±8 370±29 356±13 (135,558) 168±7 231±7 292±6 484±29 447±12 (295,376) 314±2 718±4 922± ±13 684±24 (317,114) 203±15 281±2 342±4 457±19 455±6 (355,172) 81 ±8 105±17 137±8 252±6 229±13 (360,179) 187±4 236±10 295±9 331±12 318±6 (411,20) 133±10 130±7 215±9 278±15 313±12 (551,312) 101±16 132±13 178±11 318±6 289±18 Continued on next page

149 120 Table 4.2 continued from previous page (X,Y) O Cu-L Si Ti Fe (555,465) 143±6 221±10 262±7 463±25 419±22 The majority of damaged pixels presents a curved functional dependence of the trap depths with respect to the source X-ray energy, generally showing a steep increase of the charge losses up to the silicon energy and a flattening above it. Examples are shown in Figure A power law function provides a good fit to the observed depth dependence. The best fit parameters for a subset of pixels with well determined charge losses at all energies are reported in Table 4.3. The average power law index of these fits is α=0.45, a value close to the square-root dependence of the CTI on ASCA SIS (Dotani et al., 2001), but the index is seen to vary considerably in individual cases, with values ranging from α 0.15 to α 1 in the steepest ones. For comparison, the energy dependence of the trap depths on board the XRT in Photon Counting mode is modelled with a power law index of α=0.8 (Pagani & Beardmore, 2013). No correlation is found between the power law index and the depth of the defects measured at the titanium reference energy. In a density-driven scenario the observed energy dependence is modelled in terms of the electron capture and release probabilities. At a temperature of 75 C, the charge release time is longer than the transfer time, as confirmed by the analysis of the sacrificial charge effect (see Section 4.8). Therefore, the number of electrons re-emitted during the charge transfer over one pixel is very low and can not account for the observed variations in the measured trap depth. The capture time (see Equation 1.11) is a function of the electron capture cross section, the thermal velocity of the electrons and the charge density. In CTI models the trapping mechanism is often considered instantaneous when the capture time is shorter than the charge transfer time. As an example, for titanium X-rays of kev detected with a CCD22 operated at T= 75 C, the common P-V trapping complex with a cross section of σ = cm 2 has a capture time τ c =0.9 µs, an order of magnitude less than the transfer time of15µs. Itscaptureprobabilityisthereforeclosetoone, andeachtrapcaptures an electron from the charge packet. On the other hand, the capture time

150 121 Figure 4.10: Examples of pixels with an energy dependence of the charge losses showing a steep increase up to the silicon energy and a flattening at the higher energies; this behaviour is seen in the majority of damaged pixels.

151 122 (X,Y) α Chi 2 /dof Chi 2 red (16,160) 0.39± / (66,252) 0.39± / (99,293) 0.15± / (159,400) 0.31± / (194,510) 0.80± / (262,550) 0.74± / (267,483) 1.09± / (293,438) 0.56± / (303,523) 0.33± / (359,498) 0.45± / (403,496) 0.28± / (411,406) 0.37± / (448,394) 0.39± / (450,528) 0.26± / (462,265) 0.50± / (494,336) 0.63± / (541,451) 0.36± / (559,98) 0.14± / (560,345) 0.82± / (560,484) 0.24± / (593,113) 0.35± / Table 4.3: Trap depth energy dependence. Power law indices α from the best fit of the energy dependence of the charge losses using a power law function.

152 123 Figure 4.11: Electron capture probability calculated as a function of the charge cloud density for trap types of cross section between σ = cm 2 and σ = cm 2, for a CCD22 operated at T= 75 C; the full circles are the capture probability values for electron clouds of oxygen Kα (524.9 ev), copper Lα (929.7 ev), silicon Kα (1.740 kev), titanium Kα (4.510 kev) and iron Kα (6.404 kev) fluorescence lines. increases for trap types of lower cross section and for less dense charge clouds, and becomes comparable to or longer than the transfer time. This is illustrated in Figure 4.11, that shows the capture probability, calculated for the case of a CCD22 operated at T= 75 C, as a function of the signal electron density for traps with cross section ranging between values of σ = cm 2 and σ = cm 2. The trap capturing process can therefore be used to model the measured energy dependence. The steep-to-flat dependence, seen for example in the pixels presented in Figure 4.10, can be attributed to trap types with a capture cross section of σ cm 2 or lower, as at high electron density the capture probability P c is approximately 1, and all the traps in the damaged pixel capture an electron, while for the less dense clouds of oxygen Kα, copper Lα and silicon Kα events the capture probability is lower and only a fraction of the traps are filled.

153 124 The fit of the trap losses as a function of energy provides a derivation of the average value of the capture cross section for the traps in the damaged pixels. The function used for the fit is n(e) = N tot P c +N 0 = N tot ( = N tot ( 1 e t tr τc 1 e t tr v th neσc ) +N 0 ) V +N 0 (4.2) where n(e) is the number of traps that captured an electron from a charge cloud of energy E, N tot is the total number of traps in the damaged pixel, P c is the capture probability, N 0 is the number of traps filled by the background electron signal, t tr =15 µs is the row transfer time, V = cm 3 is the charge volume in a CCD22 (Holland, 1993), v th = m/s is the average thermal velocity of theelectrons at T= 75 C, n e = E/ω is thedensity number ofelectronsforachargecloudgeneratedbyanevent ofenergye,ω issetto3.68 at T= 75 C, and σ c is the capture cross section. The fit was performed for a sample of pixels presenting the steep-to-flat energy dependence with charge losses well determined at all the investigated energies. The results of the fit are reported in Table 4.4. In these pixels, the best fit values of the capture cross section vary between a minimum of σ c = (0.14 ± 0.06) cm 2 and a maximum of σ c = (1.88±0.79) cm 2, with a mean value of σ c,mean = cm 2, and a median of σ c,median = cm 2. A second type of energy dependence frequently found in this analysis consists of traps with similar depth at all energies (Figure 4.12). This dependence is commonly seen in, but not limited to, the shallower traps, with depth below 40 ev. In density-driven models similar charge losses for different signal intensities can be accounted for by a trapping time shorter than the transfer time for all the investigated charge densities, resulting in a high capture probability, such that all trap energy levels are filled by the charge packet during the transfer through the damaged pixel. As can be inferred from Figure 4.11, such traps types are characterised by high capture cross sections, with values of σ cm 2 or higher Finally, some defects (for example in pixels (42,175), (119,293), (236,491))

154 125 (X,Y) σ c (10 15 cm 2 ) Chi 2 /dof (16,160) 0.52± /2 (66,252) 0.71± /2 (99,293) 1.31± /2 (159,400) 0.95± /2 (194,510) 0.14± /2 (262,550) 0.40± /2 (293,438) 0.33± /2 (303,523) 0.58± /2 (323,381) 0.60± /2 (348,415) 1.23± /2 (359,498) 0.62± /2 (403,496) 0.67± /2 (411,406) 0.46± /2 (421,354) 0.47± /2 (448,394) 0.85± /2 (450,77) 0.59± /2 (454,332) 1.00± /2 (462,265) 0.38± /2 (494,336) 0.18± /2 (541,451) 0.78± /2 (559,98) 1.88± /2 (560,484) 0.34± /2 (593,113) 0.48± /2 Table 4.4: Capture cross section σ c for a sub-sample of pixels with a steep-toflat energy dependence of the trap depth. The cross section values were derived by fitting the measured charge losses as a function of the charge density of the X-ray events.

155 126 Figure 4.12: Examples of pixels presenting a flat energy dependence, typical of the shallower traps. In density models these traps are characterised by high capture cross sections resulting in a high electron capture probability even for low density charge clouds. show an energy dependence that resembles a step function, with a shallow depth at low energies and a sudden jump to higher charge losses at higher energy values (Figure 4.13). The interpretation of this effect is not straightforward in a density-driven scenarios. These defects could be characterised by low capture cross sections (σ < cm 2 ) and therefore low capture probabilities, so that a very limited number of electrons of low density charge clouds is captured, and only higher density charge clouds lose a substantial fraction of electrons during the packet transfer. 4.8 Sacrificial charge analysis The measured charge losses in radiation damaged pixels are a function of the observed source flux. If two or more X-rays are detected in the same column during an exposure, electrons of the signal of the first X-ray transferred over the defect centres will be trapped; these trapping sites will remain filled until the electrons are re-emitted. If the detrapping time is longer than the frame readout time, as is generally the case for all but the traps with the shallowest energy levels, the following X-rays will find the trapping levels occupied and will not be subjected to charge losses. To study the effect of the sacrificial charge packet (SCP, Gendreau et al. (1993)), two datasets were recorded collecting silicon Kα X-rays at T= 75 C and T= 100 C, with the highest flux allowed and sustainable by the exper-

156 127 Figure 4.13: The trap depth in a set of pixels resembles a step function with respect to energy; this effect can be explained in density models by traps with very low capture cross sections, so that only high density packets lose a substantial fraction of electrons. imental apparatus, 3 times higher with respect to the standard value used in the energy and temperature dependence investigation. This allowed the detection of an average of 3 X-ray photons per frame in a column. As the X-ray illumination is uniform, the trap analysis technique I have developed can reveal the flux dependence statistically, as only in a fraction of the collected frames 2 or more X-ray photons will be transferred through a column with a deep trap. Energy profiles along columns with previously identified traps were extracted and compared to the profiles from standard, lower flux samples. This procedure showed, independently of trap depth, an apparent, faster recovery of the event energies in rows above traps in datasets of higher flux at both T= 100 C and T= 75 C (Figure 4.14). This unexpected result, after close investigation, was revealed to be an artifact of the procedure used to derive the energy profiles, caused by the fit with a Gaussian function of the measured X-ray energies; a faster recovery at higher fluxes would indicate that only a fraction of the trap levels in a cluster remains filled during the frame readout, even at the coldest T= 100 C setting. A detrapping time shorter than 1 ms would imply the presence of a large number of traps with very shallow characteristic energy levels of 0.3 ev, excluding for example the phosphorous-vacancy (P-V) complex, in contrast to what was found from the temperature dependence analysis. To investigate the unexpected result in more detail, we refined the analysis procedure by distinguishing in each frame the first X-ray event transferred

157 128 Figure 4.14: Comparison of the X-ray event energies columns with traps in datasets with an average flux of 1 event per frame per column (in black) and 3 times higher (in green). Examples from columns 304 and 559 are shown here. There is an apparent faster recovery of the event energies in datasets of higher flux. through a trap from all the following ones. Using this classification, the first X-ray event is the SCP, whose charge is partially captured by the cluster of traps when transferred over the defect site. All the following X-rays remain unaffected by the traps if the detrapping time is longer than the difference in their transfer time. This analysis revealed a more detailed and realistic picture of the flux dependence. Figure 4.15 shows the results from column 410, affected by a deep trap in pixel (410,159). In the Figure, the sacrificial X-ray events above the trap are plotted in black, while all the other events are shown in red; only the sacrificial events lose a fraction of their energy to the trap, while the following X-rays remain unaffected. This indicates that the SCPs lose electrons to the trap cluster, and the trap energy levels remain filled while the following X-ray events are being transferred. To visually enhance the observed behaviour, we separately derived the energy profiles of the sacrificial charge packets and the profiles of the following X-ray, by fitting Gaussian functions to the event energies along columns affected by traps. The comparison of the profiles highlighted the different effect of the traps on the SCPs and the following X-rays; while SCPs always lost part of their charge to the traps, the following X-rays were transferred unaffected, confirming that the trap energy levels remain filled during the readout process for both datasets at T= 100 C and T= 75 C. The detrapping times are therefore longer than the transfer time of an X-ray event through sections of

158 129 Figure 4.15: Sacrificial charge effect in column 410, that presents a deep trapping centre in pixel (410,159). In the top panel only the first X-ray events above the trap are plotted in black. These events are the sacrificial charge packets, and their charge is partially lost when captured by the trap. In the bottom panel, the X-rays following the SCPs above the trap are plotted in red. These X-rays are transferred through the damaged pixel with no loss of charge, as the trap levels remain filled while the frame is being readout. The data were taken using the silicon source at T= 75 C.

159 130 the CCD columns ( 5 ms), implying trap energy levels larger than 0.35 ev. The profile comparisons for traps in pixels (119,293) and (410,159), in datasets taken at T= 75 C, are shown as examples in Figure Two exceptions to the normal τ e > t transfer were found in the analysis for the traps in pixels (159,400) and (501,372). In these cases, in the silicon dataset at T= 75 C, the X-rays following the SCP do not fully recover their energies at the levels of pixels just below the trap (Figure 4.17), indicating a faster detrapping time for a significant fraction of the energy levels compared to the traps discussed previously. The damage in these two pixels also presents an unusual temperature dependence, with a trap depth at T= 100 C much shallower than at 75 C. The sharp reduction in depth points to a sharp transition of detrapping regimes of these traps between the two temperatures, with the majority of energy levels effectively frozen at 100 C and a much shorter emission time at 75 C. This large difference is hard to explain unless we consider the possible presence of distinct trap populations in the damage cluster with a long and short emission time scale that become dominant at 100 C and 75 C respectively; alternatively, the observed behaviour can be due to a longer than usual trap capture time in these clusters, that at the coldest temperatures ineffectively trap electrons as they are transferred over the damaged pixels Sacrificial charge in datasets with standard flux The analysis of the datasets with a high source flux has shown a clearly distinct behaviour in the energy lost to trapping centres by the SCPs and the following X-ray events. Based on this result, I proceeded to study the sacrificial charge effect in the datasets of lower flux that were used in deriving the temperature and energy dependence of the traps. If confirmed in the low flux datasets, the effect would not alter the results of the temperature and energy analysis, as the trap depths were measured based on the difference in energy in the pixels just below and above the defects, but it would be significant in the derivation of the trap energy levels based on the emission time calculation that is presented later in Section 4.9. Moreover, we are particularly interested in determining above what temperatures the sacrificial charge effect is not seen anymore, as this would provide an upper limit to the detrapping times.

160 131 Figure 4.16: Energy of the silicon Kα SCP X-rays (in black) and of the following X-ray events (in green), fitted with a Gaussian function along columns affected by traps in pixels (119,293) and (410,159). The energy profiles, from the dataset taken at T= 75 C, show that electrons from the charge cloud of the first X-rays transferred over the damaged pixel are captured by the trap, while the following X-rays are transferred without losses.

161 132 Figure 4.17: In pixels (159,400) and (501,372) the effect of the sacrificial charge is reduced, and the energy of the following X-ray events is only partially recovered. For these damage clusters the detrapping time appears faster than for other traps in the CCD. For this purpose, we reprocessed all the titanium datasets taken at temperatures between T= 100 C and T= 50 C, identifying and separating the first events above the traps from all the following X-rays in each column. With an average flux of 1 event per column per frame the likelihood of detecting two or more events in the same column is quite substantial ( 26%) and in fact for all the columns multiple X-ray events per frame were detected even for traps in pixels near the top of the detector. In Figure 4.18 we show the color-coded plots relative to column 410 at T= 75 C, that was presented earlier for the silicon dataset with higher flux in Figure The statistics are high enough to allow the fitting of the titanium line along the columns of the events following the SCPs for most of the deeper traps, with the exception of the first few pixels above a trap for which the likelihood of detecting two or more events in the same frame is very low. For the datasets at T= 100 C and T= 75 C, similar results as in the higher flux datasets were found, with well separated energy profiles for the SCPs and the following X-rays. The same behaviour was seen for datasets taken at temperatures up to T= 60 C. At 55 C the separation started to became less clear for most traps and at 50 C the profiles became indistinguishable. In Figure 4.19 the results of the analysis are presented for a characteristic sample of columns. The results ofthistest confirmthatfortemperatures colder thant= 60 C the detrapping time remains longer than the characteristic readout time of the charge packets over the detector. On the other hand, at warmer temperatures

162 133 Figure 4.18: The sacrificial charge effect is also detectable for the datasets with the standard flux of 1 event per column per frame that were collected for the temperature and energy dependence analysis. The results for column 410 at T= 75 C are shown here, with SCP events in black and the following X-rays in red. Events at 4700 ev are from the titanium Kβ line.

163 134 Figure 4.19: Sacrificial charge effect for a sample of typical traps (from left to right, pixels (99,293), (261,280), (323,381)) as a function of temperature (from top to bottom, T= 100 C, T= 75 C, T= 55 C, T= 50 C). The energy profiles of the SCPs are well separated from the profiles of the X-ray events following the SCPs for the coldest temperatures, but become indistinguishable at 50 C.

164 135 Figure 4.20: Partial reduction of the energy lost to the traps in column 132 (a manufacturing defect, left panel) and column 410 (damage caused by protons, right panel). For X-ray events detected further up above the trap in a column the number of electrons lost in the damaged site is reduced. The observed behaviour is shown here at three temperatures, 75 C, 65 C and 55 C. A binning of 20 pixels was used to generate the energy profiles. the energy levels of the trapping sites are emptied over the transfer time of a few tens of pixels, that is, τ e t transfer. For example, the charge transfer time over a segment of 20 pixels is t transfer = µs = 0.3 ms; emission times of 0.3 ms or lower at T 60 C correspond to trap characteristic energy levels of 0.4 ev or lower. This result is in agreement to what was derived from the analysis of the temperature dependence of the charge losses. 4.9 Trap emission time constant The recovery effect seen in some of the columns affected by traps in the XRT corner source data (Section 3.4.2) is also evident in the analysis of the data collected in the laboratory using the proton damaged CCD. The effect is illustrated in Figure 4.20 for two deep traps, the manufacturing trap in column 132 and the proton damaged pixel in column 410; the manufacturing trap is presented as its extreme depth highlights the effect, that nonetheless is clearly present in many shallower traps. As already noted from the analysis of the XRT data, the amount and shape of the recovered signal is a function of temperature, with a slower, more gradual recovery as the CCD temperature is lowered. The observed behaviour is interpreted in terms of the detrapping process

165 136 of electrons captured during a frame readout, with Equation 3.3 providing the number of filled traps n(t) in the cluster after the transfer of t rows. When the function is used to model the observed recovery, the average emission time of the damage cluster is derived from the fit. In light of the results from the sacrificial charge effect discussed above, only the first X-rays detected above a trap in a frame were selected to extract the titanium line energy profiles along CCD columns. Binnings of 1, 5 and 10 pixels per datapoint were implemented, and the line was fitted with a Gaussian plus a constant function. The IDL MPFIT package was used to derive the best fit of the observed recoveries using Equation 3.3 to determine τ e. In the initial attempt, 1 pixel profile binning was used, but apart from the deepest traps the recoveries were too noisy to yield a meaningful fit to the data. The fits were next repeated using profiles with binning of 5 and 10 pixels around the central pixel to improve the signal to noise ratio. For the deepest traps, the binning of 5 pixels provided best fit parameters consistent with the ones derived from 1 pixel binning and was therefore used for the fits of the remaining trap profiles as well. The recovery effect is detected in all the manufacturing traps(section 4.5). For the proton-generated traps, the effect is only seen for the deepest damage clusters, while the uncertainties in the energy measurements, the stochastic nature of the process and the global CTI losses prevent the identification and the characterisation of the recovery for the majority of the shallower defects, at the highest temperatures in particular. Figure 4.21 presents examples of the fits to the recovery profiles in a selection of deep, proton-generated traps in columns 410, 421, 560 and 593. The trap emission time constant was derived from the fits. Its mean value, in terms of transfer time units (15 µs), is 7.6 at T= 50 C, and it increases to τ em =18.5 at 60 C and to 26.5 at 70 C. As an example, in Figure 4.22 the best fit emission times for the proton damage in pixel (410,159) as a function of temperature is shown. The emission time is related to the characteristic energy of the trap type E t through Equation The values of E t derived varying the trap cross section in an interval between σx = (the A-centre defect) and σx = (the divacancy defect) ranged from 0.26 and 0.35 ev, very close to what measured for the damaged pixels in the XRT corner source data. The

166 137 Figure 4.21: Fits to the recovery profiles for a sample of proton-generated traps in columns 410, 421, 560 and 593, from the titanium dataset at 75 C. The profiles have been extracted using a 5 pixels binning. Figure 4.22: Detrapping time as a function of temperature derived from the fits to the recovered energy profiles for the trap in column 410. As expected, the emission time is higher at colder temperatures.

167 138 (X,Y) E(σX = ) E(σX = ) E(σX = ) (042,175) 0.283± ± ±0.022 (099,293) 0.281± ± ±0.010 (119,293) 0.302± ± ±0.012 (132,104) 0.299± ± ±0.002 (133,255) 0.302± ± ±0.002 (134,407) 0.303± ± ±0.002 (251,332) 0.284± ± ±0.004 (295,376) 0.274± ± ±0.005 (317,114) 0.286± ± ±0.001 (323,381) 0.297± ± ±0.012 (355,172) 0.266± ± ±0.009 (360,179) 0.283± ± ±0.005 (410,179) 0.269± ± ±0.008 (411,020) 0.303± ± ±0.006 (421,354) 0.284± ± ±0.012 (461,072) 0.277± ± ±0.012 (551,312) 0.288± ± ±0.005 (555,465) 0.288± ± ±0.002 (559,098) 0.268± ± ±0.012 (560,484) 0.282± ± ±0.011 (593,113) 0.283± ± ±0.005 Table 4.5: Energy levels of the traps in the damaged pixels, as derived from the best fit of the energy profile recoveries, for a sample of representative values of σx. energy levels for representative values of σx are reported in Table 4.5 for all the analysed traps (both proton-generated and manufacturing). Based on the analysis of the temperature dependence (Section 4.6) and of the sacrificial charge effect (Section 4.8), low energy levels (E<0.3 ev) seem unlikely, or at least constitute a minority of the traps population. On the other hand, the P-V trap is characterised by an energy level E=0.46 ev that seems too high with respect to the values derived from the recovery fits (Table 4.5). The Carbon-Vacancy-Oxygen complex (C-V-O) with energy level of 0.38 ev is a good candidate given the derived trap properties. Alternatively, a mix of P-V complexes and lower energy trap types can explain the observed behaviour of the damaged pixels.

168 139 Figure 4.23: Temperature dependence of two manufacturing defects in pixels (132,104), left panel, and (317,114), right panel, showing a flattening of the damage depth at colder temperatures or an additional drop at 100 C Manufacturing induced traps The investigation of a pre-proton irradiation dataset presented in Section 4.5 has revealed the presence of manufacturing defects in columns 132, 133, 134, 135, 295, 317, 355, 360, 380, 381, 411, 551, 555 and 593. These traps are generally very deep (> 250 ev), with the exception of a few cases (in columns 380, 381, 593)withadepthbetween 50and100eV.Thesedefects wereincluded in the analysis performed to characterise the CCD radiation damage, and the results can be compared to the proton-generated traps. Manufacturing defects present either a flattening of the damage depth towards lower temperatures, as in pixels (132,104), (133,355), (295,376) or in some cases show a decrease in the depth at T= 100 C compared to that seen at higher temperatures, as the case of pixels (317,114), (355,172). Examples of the two observed behaviours are shown in Figure As in the case of proton-damaged traps, thermally generated charge partially filling the trap energy levels lowers the depth of the damage cluster at high temperatures. At lower temperatures, the turnover is instead due to the longer emission time, that keeps the traps filled once charge is captured. All the manufacturing defects present a curved energy dependence, that is also common in proton induced traps, with an initial fast increase in the amount of lost charge as a function of energy and a slower increase at the highest titanium and iron datapoints; for many pixels, the peak of the defect depth is already reached at the titanium measurement, and the energy dependence is

169 140 Figure 4.24: Energy dependence of two manufacturing defects in pixels (133,255), left panel and (360,179), right panel, displaying a curved dependence on X-ray energy, with a flattening at titanium and Iron. flat above it. Two examples are shown in Figure The curved dependence can be fitted with a power law function, as was done for the proton-generated traps (Section 4.7). The average value of the power law index is ᾱ=0.36±0.05, consistent with that derived from the traps generated by the proton irradiation, which showed a curved energy dependence with ᾱ=0.45±0.06. The investigation of the sacrificial charge effect from the analysis of the datasets with high flux ( 3 times the standard count rate) was straightforward thanks to the very large depth of the manufacturing traps, which enhanced the difference in energy of the first X-ray event above a trap from those following. The results were consistent with what has been found for the proton damage traps, with damaged sites remaining filled after the capture of charge from the first X-ray packet during the frame readout process. As an example, in Figure 4.25 the analysis of the manufacturing defect in pixel (555,465) is presented, showing the measured energies of the first X-ray events in black and of the following X-ray events in red, along with the comparison of the energy profiles extracted by fitting the X-ray energies in the pixels along the column. The results of the analysis of the sacrificial charge effect were clear even for the datasets with the standard lower flux, and allowed an investigation of its temperature dependence from the processing of the titanium samples taken between 100 C and 50 C. At the lowest temperatures, the energies of the SCPs and the following X-rays are well separated for all the manufacturing traps. At higher temperatures, and in particular at 50 C, the two profiles become very hard to distinguish, in part due to the reduced trap depth as

170 141 Figure 4.25: The sacrificial charge effect for manufacturing trap (555,465), in the dataset taken at T= 75 C, with SCP events in black and the following X-rays in red (left panel), and the profiles derived by fitting the energies along the columns by a Gaussian plus a constant function (right panel). the CCD is warmer and in part because of the lower statistics at the lower count rate, in particular in pixels adjacent to the damaged sites. As in the case of proton-generated traps, the two profiles overlap after a few tens of pixels above the damage (see for example Figure 4.26), revealing that the electrons captured from the first sacrificial X-ray event by the traps have been re-emitted over those timescales; this would correspond to trap energy levels of the order of 0.40 ev or lower. An interesting feature of the manufacturing defects is their spatial extension. The investigation of the trap properties includes the exact identification of the damaged pixel, that is, the row in which the charge is lost. For the shallower traps in particular this task is problematic because of the limits on the X-ray photon statistics, the stochastic nature of the charge capture and release processes and the intrinsic spectral resolution of the detector. In practice, the localisation of the damage was performed manually for each trap, using varying binning values at the different temperature settings from the titanium and iron datasets, and deriving the value of the row coordinate that yielded the Gaussian fit lowest energy. For the proton-induced traps, the energy profiles derived with this process show a drop in energy at the damaged pixel, followed by a flattening or a recovery above the trap. In manufacturing defects, instead, multiple neighbouring pixels appear to cause charge losses during the X-ray transfers. As an example, in Figure 4.27 the energies of the X-rays detected in pixels close to the defect

171 142 Figure 4.26: Energy profiles of the SCPs and the following X-rays in column 355, extracted for datasets taken at 75 C (left panel) and 50 C (right panel). While the profiles are well separated at 75 C, at 50 C the energies overlap within the uncertainties, revealing an emptying of the trap energy levels on timescales of the order of a few tens of row transfers at the warmer temperature. in column 132, in the titanium dataset at T= 70 C are presented, showing a first loss of approximately 100 ev in row 103 followed by an additional loss of 300 ev in pixel 104. A similar behaviour is seen in the other deep fabrication traps. The charge trapping over multiple pixels are attributed to localised potential barriers in the channels caused by the generation of defects during the fabrication process (Janesick, 2001). A search for proton damage defects extending over more than one pixel was also carried out but no clear evidence was found; proton damage in columns 408 and 485 appeared extended over 2 pixels in datasets at 70 C, but this was not confirmed when checking the result with datasets at other temperatures. The low statistics, when energies are derived from X-rays detected in a single pixel, puts severe limitations in the analysis, with typical uncertainties of ev, so that the shallowest difference in charge losses detectable in 2 neighbouring pixels is of the order of 50 ev. Overall, the analysis of the datasets collected in the laboratory did not reveal a significant difference in the properties of the manufacturing defects and the proton-generated damage. Fabrication defects are generally deeper and in the deepest cases are extended over 2 or more pixels. When measured using titanium X-rays at 75 C, most fabrication defects are larger than 250 ev, while the depths were well below that value for all the pixels damaged by

172 143 Figure 4.27: Manufacturing defects show evidence of spreading over 2 or more pixels, as in the case for the damage in column 132. On the left panel, the measured energy of the X-ray events detected in rows 102, 103 and 104 are plotted in black, blue and red, respectively, and show an initial loss of 100 ev in row 103 followed by an additional loss of 300 ev in pixel 104. The spreading of the traps over two pixels can also be seen in the energy profile derived with 1-pixel binning (right panel). protons Summary The damage of the proton-irradiated CCD22 has been characterised with a dedicated laboratory program at the Camera test facility of the Space Research Centre. The aim of the study was to identify different charge trap populations and to describe their properties by measuring the temperature-, energy- and flux-dependence of the charge lost to traps in individual damaged pixels. The majority of traps presents the deepest energy losses between 70 C and 75 C. Different temperature dependencies were observed; while a small number of shallow traps present a flat dependence, most damaged pixels present a curved dependence, with reduced charge losses at warmer regimes. The observed behaviours were interpreted in terms of an increased thermal current signal at the higher temperatures and longer detrapping times when the temperature is decreased, the trap freezing effect. These results are consistent with what has been found for defects generated on board the XRT. Characteristic trap energy levels of 0.35 ev or larger were derived for the trap population with a curved temperature dependence.

173 144 The energy dependence in most damaged pixels is characterised by a steep increase at the lower energies followed by a more gradual increase or a flattening at higher energies. This general behaviour can be interpreted in terms of a density-driven model that assumes a fixed charge packet volume and a higher electron density at higher energies. The electron capture time constant is longer for less dense charge clouds, resulting in a lower capture probability, so that at lower energies a smaller fraction of electrons is trapped in the damaged pixels during the charge transfers. The flux dependence highlighted the sacrificial charge effect in all but a few damaged pixels, by comparing the energy lost by the first X-rays transferred through trap sites in a frame with the unaffected following X-rays. The observation of this effect at a variety of temperatures set limits on the trap emission times and therefore on the average characteristic energy level, implying values of between 0.30 and 0.40 ev for the majority of the traps in the clusters. The most damaged pixels present an apparent reduction of the charge captured for X-rays that are detected farther above the trap in the column. This effect was modelled in terms of the generated thermal current signal captured and re-emitted during the frame transfer process by the trapping sites at different temperatures. The emission times derived from the fits implied characteristic trap energy levels between 0.25 and 0.40 ev for a set of standard trap parameters. A number of manufacturing defects predating the proton irradiation have been identified. Their properties in terms of temperature, energy and fluxdependence do not differ from the population of proton-generated traps. However, on average, manufacturing defects cause larger charge losses compared to proton traps.

174 Chapter 5 Neutron irradiation and damage 5.1 Introduction The analysis of the radiation damage on the CCD on board the XRT and on the detector irradiated with 10 MeV protons has revealed that in both cases defects were generated that caused large energy losses in the affected pixels. This result is surprising, in particular for the proton irradiation, that is expected to cause a small number of displacements in the interaction with the silicon nuclei (Kinchin & Pease, 1955). Predictions on the degradation of the XRT CCD in orbit were made before launch based on the Swift radiation environment and the transmitted proton spectrum behind the aluminium shield (Short, 2000) that protects the detector from space radiation. A possible second source of radiation damage on board the XRT is due to the flux of secondary neutrons generated by proton interactions in the aluminium shield. Unlike protons, the direct interaction of neutrons with the silicon nuclei can lead to many displacements in the lattice and to the formation of a damage cluster at the affected location, resulting in large energy losses. To investigate this scenario, a laboratory project was carried out with the goal of characterising the damage to individual pixels of the CCD caused by high-energy neutron irradiation. This chapter describes the neutron damage study. Firstly, the interaction ratebetween the secondary neutronand thesilicon lattice of theccd onboard the XRT is estimated; the Swift space environment is modelled using SPENVIS, ESA s Space Environment Information System, and it is used to derive the flux 145

175 146 of secondary neutrons behind the shield and the rate of neutron interactions with the silicon atoms of the detector; the estimated neutron flux is refined by adopting a 3D geometry of the proton shield and by using GRAS, a Geant4 based space radiation analysis tool. Neutron irradiation of the proton-damaged CCD at the Frascati Neutron Generator in Italy is described, and the laboratory program conducted at the Leicester Camera test facility to investigate the neutron damage sustained by the detector is illustrated. The results of the neutron damage analysis and the comparison with the damage observed on board the XRT and after the proton irradiation are presented. 5.2 Exposure of the XRT CCD to secondary neutrons Neutron flux estimate The Swift exposure to space radiation has been estimated using the online tools to model the space environment and its effects available in SPENVIS, ESA s Space Environment Information System. SPENVIS uses the AP8 and AE8 trapped protons and electrons models (Vette, 1991), developed by the US National Space Science Data Center at NASA s Goddard Space Flight Center. These models are based on historical measurements by more than 20 satellites starting in the 1960s, and consist of maps of proton and electron fluxes at different energies in the Earth s radiation belts. Details of the models and their implementation can be found in the Documentation archive available on the SPENVIS website. The initial setup in SPENVIS consists in the definition of the spacecraft environment. This is achieved by uploading a recent Swift TLE (Two-line element) ephemeris file available from the CELESTRAK website 1 to generate the satellite trajectory. The average proton and electron radiation spectra are derived from the exposure of the satellite over the period of one day (corresponding to 15 Swift orbits). Figure 5.1, left panel, shows the map of the integral flux calculated using the AP8 model for protons of energy of 0.1 MeV or above. Most of the exposure to fast protons is concentrated inthe SAA, so it 1

176 147 is essentially the length of the SAA passages that defines the dose accumulated while in orbit. This can also be seen from the right panel of Figure 5.1, that presents the proton flux (E p 0.1 MeV) as a function of time during a period of 1 day. In Figure 5.2 the differential and the integral proton spectra averaged over a one day period are shown. Figure 5.1: (Left panel) - Map of protons with energies above 0.1 MeV derived for the Swift orbit over a period of one day using SPENVIS s online tools; most of the proton dose is accumulated during the SAA passages. (Right panel) - Flux of E p 0.1 MeV protons as a function of time over a period of 1 day, with peaks corresponding to the times spent by the spacecraft in the SAA, as derived by SPENVIS. The average proton flux is used as input to determine the spectrum of the protons and of the secondary neutrons emerging from the interior surface of the radiation shield. For the XRT, the shape of the shield is quite complex, conical at the top and bulbous at the bottom, as can be seen in the technical drawing of Figure 5.3. In SPENVIS, the shielding can be modelled specifying the thickness of the aluminium surrounding the detector. Using a thickness of 18.5 mm (the largest possible using SPENVIS s online tool, but lower than the true value by a factor of 1.5-5, depending on the incident proton trajectory) as an approximation of the irregularly shaped XRT proton shield, the protons and secondary neutrons spectra reported in Figure 5.4 were derived from Monte Carlo simulations using standard electromagnetic (EM) processes and hadronic nuclear interactions. The estimated integral flux of neutrons emerging from the shield is F n = 1.5 neutrons s 1 cm 2 ; this value should be considered as a lower limit, as a thicker aluminium layer, closer to the real thickness of the shield, would result in an increased flux of neutrons emerging from behind the shield.

177 148 Figure 5.2: Integral and differential spectrum of the trapped protons in the Swift orbit averaged over a one day period, derived using SPENVIS. A more comparable model of the neutron rate is presented in the subsequent sections of this chapter. Figure 5.3: Technical drawing of the XRT aluminium shield (drawing E-SWT- 9748, Lower proton shield ). Refined shield geometry and neutron fluence The estimate of the number of secondary neutrons reaching the CCD can be refined by improving the modelling of the shield from the simple 2D slab to

178 149 Figure 5.4: Integral and differential flux of the protons (top panel) and secondary neutrons (bottom panel) behind 18.5 mm of aluminium shielding, derived through Monte Carlo simulations using SPENVIS. a more realistic 3D geometry, using applications based on Geant4 2, a toolkit for the simulation of the passage of particles through matter. The Geometry Generation Tool 3, a software developed by the SPENVIS group of the Belgian Institut for Space Aeronomie, was used to create the 3D shielding design

179 150 The software exports the 3D geometry in the Geometry Description Markup Language (GDML) format, a XML based meta-language, used by the Geant4 analysis software. The 3D geometry software allows the user to choose between a set of shapes to build the geometry model in a hierarchical structure of parent-child relations in a tree, starting with the World node, where all the shapes of the model havetobeincluded. Forour3Dshieldmodel, the World issettobeavacuum cube with a side length of one meter. The detector is defined as a silicon box with a surface of cm 2, and is positioned at the origin (0,0,0) of the World coordinate system. The shield, positioned with the orientation of Figure 5.3, is modelled using a series of aluminium cylinders: a hollow cylinder at the bottom, with a height of 88 mm, an outer radius of 60 mm and an inner radius of 46 mm; a second hollow cylinder in the middle section, with a height of 29 mm and outer and inner radii of 76 and 55.5 mm; a cylinder at the top, of 27 mm height and 60 mm radius. The 3D model, shown in Figure 5.5 from two viewing angles, is a much better approximation of the XRT proton shield than the simplistic 2D slab of aluminium used in the initial SPENVIS modelling. Figure 5.5: 3D model of the XRT shield, designed using the Geometry Generation Tool software, as seen from two different viewing angles. The shield is modelled with a series of aluminium cylinders, and the detector, inside the central hollow cylinder, modelled as a silicon box. The fluence of secondary neutrons was estimated using the Geant4 package

180 151 GRAS (Geant4 Radiation Analysis for Space, Santin et al. (2005)), version 3.1, a 3D space radiation analysis tool. GRAS accepts as input a 3D geometry, like our 3D model of the XRT shield, and an incident particle source. In this study, the spectrum of the trapped protons in the Swift orbit derived using SPENVIS was set as the particle source, using a spherical source geometry with a radius of 10 cm and an omnidirectional angular distribution. GRAS uses the Geant4 toolkit to model the particle interactions, including both electromagnetic and hadronic processes, within the volume of the input 3D geometry. For this study, one-hundred thousand incident particles were used in the Monte-Carlo simulation to estimate the fluence of selected particles at the boundaries between volumes of the defined geometry, with the goal of deriving the number of incident neutrons on the detector surface. The simulation was run a first time in the described configuration to calculate the number of protons incident on the surface of the shield. The yearly proton fluence was derived to be protons, or, with a shield surface of 935cm 2, of protonspercm 2. ThisvalueisafactorR=1.5lower than the yearly trapped proton fluence of cm 2 calculated by SPENVIS for the Swift orbit. The simulation was then repeated to evaluate the yearly neutron fluence on the CCD, obtaining a yearly total of 5.29± neutrons. Scaling the value for the R = 1.5 factor, the estimated total fluence of secondaryneutronsincident onthedetectorduringa5-yearmissionis neutrons, that divided by the CCD area corresponds to a flux of neutrons s 1 cm 2. The GRAS simulation additionally provides the spectrum of the secondary neutrons emerging from the shield, that along with the estimate of the neutron fluence can be used to design laboratory irradiation experiments and in the quantitative interpretation of the measured CCD damage Rate of interaction of secondary neutrons with silicon The rate of interaction Y of secondary neutrons with the silicon nuclei of the CCD in a volume of depth dx can be estimated from the integral neutron flux reaching the CCD surface F n and the neutron cross section in silicon,

181 152 Y = F n n Si E E 0 σ(e) dx de de. (5.1) where the integral is calculated between the minimum neutron energy E 0 and E, σ(e) is the neutron-silicon cross section and n Si is the number density of nuclei in silicon, calculated from the its density ρ Si = 2.33g/cm 3, atomic weight A Si = g/mol and Avogadro s number N A = mol 1 as: n Si = ρ Si /(A Si /N A ) = cm 3 (5.2) The GRAS Monte Carlo simulation, using the refined 3D shield geometry, provided an estimated flux of neutrons on the detector F n = neutrons s 1 cm 2. The estimated value is lower if the simpler modelling of the shield with a 18.5 mm thick slab of aluminium is instead used. The flux of secondary neutrons emerging from behind the shield estimated in this case has to be scaled by the fraction of neutrons f effectively reaching the detector. Assuming the secondary neutrons are emitted omnidirectionally and the inner surface of the shield to be the one defined by the cylinders of the refined shield geometry described earlier (A cylinders = 63.75cm 2 ), with a CCD front area of A CCD = cm 2, the flux of neutrons reaching the CCD is estimated to be approximately neutrons s 1 cm 2 ; the lower value for the simpler geometry is expected, as the aluminium shield is thinner in that case. The neutron-silicon cross section is a function of energy. From the Janis database of nuclear reaction data 4 its reported value is 2.4 barn at 1 MeV and 1.85 barn at 10 MeV, but for simplicity in this calculation we assume a constant value of σ = 2 barn. The charge cloud generated by the absorption of an X-ray photon is confined in the case of the XRT CCD in a volume under the electrodes of depth dx = 0.15µm (Holland, 1993); within this depth, the rate of neutrons-nuclei interaction is estimated at Y s 1 cm 2 or Y s 1 cm 2 forthe simple slab geometry andthe refined 3D geometry of the shield respectively. As expected from the small cross section value the rate is quite low, and moreover, the creation of stable defects might be limited to only a fraction of 4 database.html

182 153 neutron-nuclei hits. Overall, the number of damaged pixels from secondary neutrons is expected to be small, but not completely negligible, considering the length of the Swift mission; for example, over a 5 year period, the accumulated fluence of secondary neutrons reaching the detector derived with GRAS is estimated at neutrons cm 2 and the number of neutron-nuclei hits is approximately 272. The irradiation of the CCD with a dose of high-energy neutrons of the order expected in flight should therefore provide a realistic picture of the effects of secondary neutrons on the XRT. 5.3 Neutron irradiation The CCD used in the proton damage study was irradiated with neutrons at the Frascati Neutron Generator 5 (FNG) on October 2nd The facility (Figure5.6)isdescribedindetailinMartoneetal.(1994). 14MeVneutronsare generated by the exposure of a tritiated-titanium target to a beam of deuterons via the T(d,n)α nuclear fusion reaction. The generated neutrons are quasi isotropic, at a flux of neutrons/s. The neutron flux is determined by detecting and counting the α particles produced in the T(d,n)α reaction using a silicon surface barrier detector. The reported uncertainty in the flux measurement is lower than 4% at the 1-σ level. The main components of the neutron facility are shown in Figure 5.7. The deuterium ions are produced in a vacuum chamber by the electrons emitted from a cathode filament in a duoplasmatron-type ion source (Green, 1974). A Pierce extraction electrode (Pierce, 1940) and an electrostatic einzel (unipotential) lens (Adams & Read, 1972) are used to focus the beam before it reaches a 90 bending magnet that removes the molecular deuterium from the beam, leaving only the atomic deuterium. This step is needed as the molecular deuterium is of lower kinetic energy and would generate lower-energy neutrons when impinged on the tritium target. The deuterium beam is then accelerated in the tube to energies up to 300 kev. A quadrupole triplet of magnetic field of 1.5 kg focus the slightly diverging beam before the ions hit the target placed 2 m away from the accelerating tube. The target (Figure 5.8, left panel) consists of a titanium-tritiade, a stable compound at the temperatures 5

183 154 Figure 5.6: The Frascati Neutron Generator. and pressures at the FNG. Figure 5.7: Schematic picture of the Frascati Neutron Generator, from Martone et al. (1994). The CCD was carried to the Frascati generator by David Vernon, the Leicester Camera Test Facility Head engineer; he assisted myself and Dr. Mario Pillon, the Head of the FNG, in the preparation of the experiment. The CCD was placed on a support facing the target at a distance of 15 cm (Figure 5.8, central panel), to guarantee a uniform neutron irradiation on the camera at an angle of 0 with respect to the deuterium beam.

184 155 Figure 5.8: Photographs taken during the neutron irradiation experiment at the FNG. The tritiated-titanium target (left panel); positioning of the CCD for the irradiation (central panel); the target and the CCD pictured before the irradiation (right panel). The whole CCD (including the frame-store section) was irradiated with a total fluence of MeV neutrons cm 2, of the same order of what was used for the proton irradiation ( protons cm 2 on the left-side and twice as much on the right side of the CCD). The complete neutron spectrum was derived with the Monte Carlo N-particle transport code developed by Los Alamos National Laboratory (Cashwell & Everett, 1959) to simulate nuclear processes in use at the FNG facility. The complete spectrum, shown in Figure 5.9, includes the neutrons slowed down by the scattering within the tritiated target and the back-scattered neutrons from the walls of the bunker. 5.4 Neutron damage analysis The laboratory program carried out at the Leicester Camera Test Facility to study the proton generated traps was repeated with minor modifications to investigate the damage caused by the neutron irradiation at the Frascati neutron generator. New data was acquired with the experimental setup already described and used to characterise the proton damage (Section 4.3). In addition, a new pump system developed by David Vernon, the laboratory supervisor, was used to monitor in real time the amount of nitrogen utilised to cool the CCD and to autonomously refill the coolant tank during the data