Solar Spectroscopy. with Hinode/EIS and IRIS. Magnus Woods With thanks to David Long UCL-Mullard Space Science Laboratory

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1 Solar Spectroscopy with Hinode/EIS and IRIS 4 th Solarnet Workshop, Mullard Space Science Laboratory Magnus Woods With thanks to David Long UCL-Mullard Space Science Laboratory magnus.woods.15@ucl.ac.uk

2 Why we make observations Observations are measurements Fit existing models/theories/interpretations Provoke new models/theories/interpretations Models/theories/simulations no use in isolation Need to be able to understand what we re seeing Account for errors/uncertainties Determine densities/temperatures Different processes produce different signatures using different emission lines

3 Remote sensing We obtain information about the object by looking at it Study the different types of radiation emitted by the Sun and associated phenomena But.. Earths atmosphere only transmits radiation in radio & visible bands (with distortion) Need to go to space to observe sub-mm, UV, IR, X-rays & γ-rays

4 Ground-based observing Pros: Easy to do Low cost Can get lots of data at very high cadence & resolution relatively easily Observers: Michiel van Noort and Luc Rouppe van der Voort, Oslo. Images taken using the Swedish Solar Telescope, courtesy of The Royal Swedish Academy of Sciences & The Institute for Solar Physics

5 Ground-based observing Cons: Weather-permitting ~8 hours observing/day Atmospheric effects Limited spectral coverage (radio, visible, near IR, near UV) Telescope owners/observers tend to keep the data (starting to change) No consensus on data management/storage Other assorted artefacts.. Kanzelhöhe Observatory

6 Balloon & Rocket flights Pros: Above (most of) the atmosphere Wider wavelength range available Relatively cheap Good for testing new equipment/instruments Cons: Very short duration ~Minutes for rockets, ~Days/weeks for Balloons Data recovery can be tricky Weather dependent No responsiveness to solar conditions Size/mass constraints

7 Space-based observing Pros: Can observe all parts of the spectrum Can get ~24/7 coverage No atmospheric effects No weather effects Cons: Expensive! $10,000/lb (~$4536/kg) Size & mass constraints Cannot be fixed once launched (with the exception of Hubble..) Limited data downlink (with the exception of SDO) Rigorous testing & quality control

8 Spectrograms Separate radiation out by wavelength Spatial (x,y) and spectral (λ) information in one image

9 Overlappograms! Spreading wavelengths out means spatial & spectral information gets confused Detectors still 2-D How to get x, y, λ into one image..

10 Spectrometers Take a 1-D slice of the image Disperse that in the direction perpendicular to the slit Remove confusion in direction of dispersion But, it can be difficult to identify where we re observing

11 Operated by JAXA, in collaboration with UK(MSSL) and United States Launched in Three Science instruments: SOT - Solar Optical Telescope XRT - X-ray Telescope EIS - Extreme Ultraviolet Imaging Spectrometer

12 Extreme ultraviolet Imaging Spectrometer (Hinode/EIS)

13 Extreme ultraviolet Imaging Spectrometer (Hinode/EIS)

14 Extreme ultraviolet Imaging Spectrometer (Hinode/EIS) 1 & 2 slits or 40 & 266 slots, y ~ 512 λ ~ Å & Å 1 per pixel (spatial) ~22mÅ per pixel (spectral)

15 Creating an image

16 Temporal variation Can see temporal variation of corona at different temperatures

17 EIS Spectrum

18 What else can we do? Fit the intensity in each pixel using a Gaussian Assumes thermal (Maxwellian) distribution of particle velocities along the line-of-sight A = peak intensity λ 0 = rest wavelength σ = line width ξ = thermal width Mechanical motion not associated with Brownian motion May be combination of wave or non- Maxwellian motion

19 What else can we do? If two different electron transitions for same ion have common lower level May be able to estimate density Take ratio of line intensities & compare to theoretical values Need to choose ratio to match density range! Young et al., 2007

20 Doppler velocity Most of the emission here comes from the strong emission line (red) Also a weaker component of the same line Dopplershifter to shorter wavelengths Can work out the difference between the two fits & derive the Doppler velocity of the line Repeat this for each pixel

21 Doppler velocity Doppler velocity very useful for getting three-dimensional plasma variation Waves, flows, filament eruptions etc. Harra et al. (2011)

22 Non-Thermal velocity The width of a spectral line can be described as: Observed Width = Thermal Width + Instrumental Width...+ Non-thermal Width Non-thermal velocities (line width) can possibly indicate activity in the plasma, in the absence of a significant intensity increase Wavelenth (Angstrom)

23 Where to get the data? Search for EIS data on MSSL/EIS website: Want to use the data archive search tool: Set dates/times for your search When you re happy, submit!

24 Selecting and downloading data After submitting your query, a list of all available data in your time range will be given. Use quicklook thumbnails to check which lines are in observation

25 Selecting and downloading data After submitting your query, a list of all available data in your time range will be given. Use quicklook thumbnails to check which lines are in observation

26 Selecting and downloading data After submitting your query, a list of all available data in your time range will be given. Right click to download Use quicklook thumbnails to check which lines are in observation

27 So you have the data, now what? The data you have downloaded is level-0. This is not science ready! We need to calibrate it! In SSW we will make use of the eis_prep routine: Flags saturated, warm/hot and dusty pixels Dark Current and Comic Ray correction IDL> eis_prep, filename, /quiet, /retain, /default, /save Can run multiple L0 files through this Outputs are calibrated L1 files and corresponding error files. We can work with these!

28 X-FILES We can quickly check our data and access the header information using IDL> xfiles

29 X-FILES We can quickly check our data and access the header information using IDL> xfiles Control search dates Set file type Set desired directory Choose desired file from list

30 X-FILES We can quickly check our data and access the header information using IDL> xfiles Control search dates Set file type Set desired directory Choose desired file from list

31 X-FILES We can quickly check our data and access the header information using IDL> xfiles

32 Fitting(1) We first need to load in the data in the desired wavelength window: IDL> windata=eis_getwindata( Path_to_file/your_file.fits, line, /refill) Line is the wavelength of the desired spectral window. Use xfiles to show available windows. Sometimes the fit windows can be too large and contain multiple lines. To select the part of the to fit, we can use: IDL> eis_wvl_select, windata, wvl_select

33 Fitting(1) We first need to load in the data in the desired wavelength window: IDL> windata=eis_getwindata( Path_to_file/your_file.fits, line, /refill) Line is the wavelength of the desired spectral window. Use xfiles to show available windows. Sometimes the fit windows can be too large and contain multiple lines. To select the part of the to fit, we can use: IDL> eis_wvl_select, windata, wvl_select

34 Fitting(2) We now have the windata and wvl_select structures. We will fit the data using the routine: IDL> eis_auto_fit, windata, fitdata, wvl_select=wvl_select This gives us the fitdata structure, containing the results of the gaussian fitting. We can view the results of the fitting using: IDL>eis_fit_viewer, windata, fitdata

35 Fitting(2) We now have the windata and wvl_select structures. We will fit the data using the routine: REMEMBER: Always be careful of how you have defined your reference wavelength! IDL> eis_auto_fit, windata, fitdata, wvl_select=wvl_select This gives us the fitdata structure, containing the results of the gaussian fitting. We can view the results of the fitting using: IDL>eis_fit_viewer, windata, fitdata

36 Corrections As with any instrument, EIS data is subject to several instrumental effects that must be corrected for. These are: Grating tilt: Small misalignment of CCD and grating axes. offset < 1 pixel Slit tilt: Slits are perpendicular to the CCD dispersion axes. Orbital variation: Line position is shifted over spacecraft orbit due to thermal changes Detector offset: Offset of ~15-20 pixels is present in the Y-direction between the SW and LW detectors

37 Advanced activities For most cases we want to extract the data displayed by the fit viewer and carry out further analysis upon it. To extract the fits for intensity, Doppler velocity and line-widths use: IDL>i_map=eis_get_fitdata(fitdata, /int, /map, /quiet) IDL>v_map=eis_get_fitdata(fitdata, /vel, /map, /quiet) IDL>w_map=eis_get_fitdata(fitdata, /wid, /map, /quiet) If non thermal velocities are wanted, use the routine: vnt=eis_width2velocity( Fe, XII, line, linewidth_data, ti_max=t) IMPORTANT: This calculation must be done per pixel eg. in a loop!

38 Advanced Activites (2) Take care when calculating Vnt for Fe XII 195Å. There is an instrumental line that need to be accounted for using: inst_wid=eis_slit_width(ypix, slit_ind=slit_ind) Here, slit_ind is a parameter that denotes which slit the observation uses. It has value 0 for 1 slit and 2 for 2 slit. Then to calculate Vnt: vnt=eis_width2velocity( Fe, XII, line, linewidth_data, instr_fwhm=inst_wid , therm_fwhm=therm_fwhm, thermal=thermal, ti_max=ti_max)

39 EIS Practical 1. Using the described method, prep the l0 eis data you have been given. 2. Using the methods described, load in and fit the file during the flare c- class flare. Use the Fe XII 192Å line. 3. Using the same methods load in and fit the file containing the pre-flare data. Again use the Fe XII 192Å line.

40 EIS Practical: Results

41 EIS Practical: Results

42 Interface Region Imaging Spectrometer (IRIS) Mostly measuring optically thick lines in chromosphere and transition region Mg II h & k, Si IV, C III, O I, Fe XII λ ~ Å Å & Å Å (FUV), Å Å (NUV) De Pontieu et al. (2014)

43 IRIS observations ~0.3 (FUV), 0.4 (NUV) per pixel (spatial) ~26Å (FUV) - 53Å (NUV) per pixel (spectral) Closest to AIA 304Å in observations

44 IRIS observations

45 Accessing IRIS data You can search for IRIS data using a handy tool created by LMSAL: This can also search for corresponding Hinode data. Very good when looking for joint observations! Once downloaded, quicklook files using: IDL> iris_xfiles

46 Accessing IRIS data You can search for IRIS data using a handy tool created by LMSAL: This can also search for corresponding Hinode data. Very good when looking for joint observations! Once downloaded, quicklook files using: Need to click search after changing any parameters Set tick box to display desired Hinode data IDL> iris_xfiles

47 Accessing IRIS data You can search for IRIS data using a handy tool created by LMSAL: This can also search for corresponding Hinode data. Very good when looking for joint observations! Once downloaded, quicklook files using: IDL> iris_xfiles

48 Data analysis IRIS data is downloaded as level 2 data, pre-calibrated This means we don t have to run it through a prepping routine: It s already science ready! Can carry out fitting using two possible routes: Route 1: Very similar to the method we used for EIS analysis Route 2: Object orientated method, which is the recommended method of the IRIS team.

49 Route 1 Written by Peter Young, these methods follow the syntax of and in some cases use the same code as the EIS Routines Load data: IDL>windata=iris_getwindata(file,line) BEWARE! This method has a known bug that should be fixed, but you need to be sure you are using the most up to date version!!! Fitting the data: IDL> eis_auto_fit, windata, fitdata, wvl_select=wvl_select

50 Route 2 (IRIS approved) This method is described fully in ITN26 ( It is actually the method underpinning Route 1. IDL> filename = my_iris_file.fits IDL> d = iris_obj(filename) Then to view the available spectral lines: IDL> d ->show_lines To then select and load in the data use: IDL> wave = d -> getlam(4) IDL> data = d -> getvar(4, /load)

51 Route 2 (IRIS approved) This method is described fully in ITN26 ( It is actually the method underpinning Route 1. IDL> filename = my_iris_file.fits IDL> d = iris_obj(filename) Then to view the available spectral lines: IDL> d ->show_lines To then select and load in the data use: IDL> wave = d -> getlam(4) IDL> data = d -> getvar(4, /load)

52 Further Analysis With data now loaded, analysis of IRIS data can be carried out. The IRIS software tree contains several routines that can be used in the further analysis of the data. These codes can be found in ITN26 s Useful codes section:

53 IRIS Practical Using the method described in Route 2, load the Si IV 1402Å and plot it Using Route 1, again load the Si IV data. Then using the routines described, fit the data and view the results.