LOFAR observations of pulsar wind nebula G and its environment. Laura N. Driessen Supervisors: Dr. Jason Hessels and Dr.

Transcription

1 LOFAR observations of pulsar wind nebula G and its environment Laura N. Driessen Supervisors: Dr. Jason Hessels and Dr. Jacco Vink

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3 University of Amsterdam MSc Astronomy & Astrophysics Astronomy & Astrophysics Track Master Thesis LOFAR observations of pulsar wind nebula G and its environment by Laura Nicole Driessen July ECTS Supervisor: Dr. Jason Hessels Associate Professor Co-supervisor: Dr. Jacco Vink Associate Professor The Anton Pannekoek Institute for Astronomy

4 LOFAR observations of pulsar wind nebula G and its environment Laura N. Driessen Astronomy & Astrophysics Master s Thesis The Anton Pannekoek Institute The University of Amsterdam

5 Stop trying to make Bull s-eye happen. It s not going to happen Regina George, maybe

6 THE UNIVERSITY OF AMSTERDAM Abstract The Faculty of Science Physics & Astronomy Department Research Master LOFAR observations of pulsar wind nebula G and its environment by Laura N. Driessen We present the calibration, imaging and analysis of LOw Frequency ARray (LOFAR) High Band Antenna (HBA) observations of the Galactic Plane. We investigate four calibration methods: prefactor, the Multi-frequency Snapshot Sky Survey (MSSS) imaging pipeline, direction-independent non-zero-phase calibration, and direction-independent zero-phase calibration. We show that direction-independent zero-phase calibration produces the best results for this field and particular observation. We use WSClean to image the observations and pybdsf to measure the flux density of sources in the field of view. Due to the low observing frequency, wide-bandwidth, and large field-of-view of the LOFAR instrument it is excellent for investigating the Galactic Plane. This is because low-frequency objects with similar morphology, such as supernova remnants and HII regions, can be differentiated due to their behaviours at different frequencies. In particular, due to their spectral indices (α, where flux density S scales with observing frequency ν as S ν α ) supernova remnants become brighter at lower frequencies (α 0.5) while HII regions become brighter at higher frequencies (α 0). Low-frequency observations are also useful for studying pulsar wind nebulae by investigating the lowest emission frequency of the electrons, which acts as a calorimeter for the total injected energy, and by measuring a range of frequencies to test pulsar wind nebula evolution models. We present an investigation of pulsar wind nebula G and supernova remnants in the field of view, using LOFAR HBA and archival multi-frequency observations. We investigate and improve the multi-frequency spectral energy distribution of PWN G We also show that, contrary to previous suggestions, pulsar wind nebula G does not have a large scale supernova remnant shell. This confirms its classification as a filled-centre supernova remnant. We discuss pulsar wind nebula G in the context of supernova remnants with significant amounts of cold supernova ejecta dust. Furthermore, we discuss other supernova remnant candidates in the field of view with LOFAR, showing that some candidates in the field are unlikely to be supernova remnants. We also present our discovery of a new supernova remnant, G , the first supernova remnant discovered with LOFAR.

7 Popular Abstract LOFAR observations of pulsar wind nebula G and its environment by Laura N. Driessen Our own Milky Way Galaxy is home to a wide range of interesting objects visible to radio telescopes: pulsars, pulsar wind nebulae, supernova remnants, HII regions, and more. Supernova remnants are the shells of dust left after massive stars die in supernova explosions. We observe supernova remnants as bright bubbles in space. A supernova explosion can also leave a pulsar behind. A pulsar is a rapidly spinning, highly magnetised star with a radius of only 10 km but with a mass about that of the Sun. We observe pulsars as flashing astronomical lighthouses. Pulsars can accelerate particles in the highly magnetised region around them, called the magnetosphere. As these particles flow off the pulsar they produce a pulsar wind. This wind fills the bubble of the supernova remnant, and when this happens the wind-filled bubble is called a pulsar wind nebula. HII regions are a different kind of Galactic object. An HII region is an area of ionised hydrogen that can also look like a bubble in space, and these are typically regions where stars are being formed. We can study these objects by observing them at low frequencies of light with arrays of antennas, called radio interferometers. In particular, both supernova remnants and HII regions can look like bright bubbles in the Galaxy. This makes it hard to tell them apart. Luckily, supernova remnants become brighter at lower frequencies, while HII regions become brighter at higher frequencies. So if we look at our Galaxy at a range of frequencies we can differentiate between objects that have a similar shape. We can investigate pulsar wind nebulae using low-frequency observations as well. By observing a pulsar wind nebula at low frequency we can investigate how it produces and accelerates particles in its magnetosphere, we can find out how old it is and how much energy it has overall, we can find out about the explosion that formed the pulsar and about the pulsar itself. For this project we processed and analysed observations of a particular pulsar wind nebula, named G , using low-frequency observations from the LOw Frequency ARray (LOFAR) radio interferometer. Processing low-frequency radio interferometric data is a challenging task, so we investigated various methods before finding the optimal strategy. We used our LOFAR observations, and archival observations at a range of frequencies, to investigate the properties of pulsar wind nebula G and show that it does not have a supernova remnant bubble around it, contrary to previous suggestions. We show that some objects that were classified as possible supernova remnants are not actually supernova remnants because we do not detect them in the LOFAR observations. We discovered a new supernova remnant, named G , with the LOFAR observations and studied it using a range of observations at different frequencies.

8 Acknowledgements I would like to thank my supervisors, Dr. Jason Hessels and Dr. Jacco Vink, for all of their help and support along the way. I would particularly like to thank Jason for helping me to apply for and choose a PhD position and for listening to my angst and whining while I was making the decision. Thank you to Maria Arias de Saavedra who answered my silliest LOFAR questions and explained selfcal at least three times in a row until I understood it. And thank you to Vladimír Domček for his expertise in X-ray astronomy. I would like to thank Dr. Gemma Janssen for supporting my research journey since I really appreciate your advice and I could not ask for a better mentor. Thank you to my sister, Dr. Brooke Driessen MD, for looking after my dog, Astro, and keeping up his instagram account while I have been overseas studying. I miss you and Astro every day. Thank you to my sister, Chelsea Driessen, for always pushing me to be my best, for encouraging my ambition and especially for pushing me out of my comfort zone just when I need it. My parents, Leo Driessen and Debra Driessen, have supported me every step of the way and have always encouraged me to just go for it. Not many parents would support their daughter moving all over the world to study astronomy. Thanks Dad for reading and editing all my essays, CVs and applications. Thanks Mum for listening to my whinging when I need it and for getting me to snap out of it when I need to. I would not be where I am today without parents like you. iii

9 Contents Abstract i Popular abstract ii Acknowledgements iii List of Figures List of Tables Abbreviations vi ix x 1 Introduction Supernova remnants Pulsars Pulsar wind nebulae LOFAR Observations and Processing HBA observations Calibration methods Pre-Facet-Cal Direction-independent calibration Calibrating the G observation The MSSS imaging pipeline Pre-Facet-Cal Direction-independent calibration Chosen calibration method Imaging Flux determination PWN G Past work Discovery Observations Low-frequency observations iv

10 Contents v X-ray observations Infrared observations Gamma-ray observations The distance to PWN G Magnetic Fields and Polarisation PSR J : the central pulsar of PWN G Supernova remnant features of PWN G The infrared dust bubble around PWN G IR-excess stellar objects A star-forming region Supernova ejecta dust The forward shock Detecting the forward shock at LOFAR frequencies Spectral energy distribution The G Field Observations Radio observations Infrared observations Supernova Remnants and candidates G Radio observations Pulsar search Infrared observations X-ray observations High energy observations Discussion The distance to G The age of G Other supernova remnants and candidates in the field of view Summary 50 A Calibration pipeline functions 53 B Measuring flux densities with pybdsf 62 B.1 Script to convert VLSSR and WSRT catalogues to a usable format B.2 Running pybdsf and aperture flux density measurements B.3 Extracting point source information from TGSS ADR and pybdsf C Searching for a pulsar in G a new supernova remnant detected with LOFAR 75 Bibliography 78

11 List of Figures 1.1 LOFAR low band antennas on the superterp located in Drenthe, the Netherlands A three colour image of shell-type SNR Cassiopeia A. Red is a high-resolution VLA observation at 4.7 GHz. Green and blue are both Chandra X-ray observations. Blue shows kev X-rays showing the synchrotron continuum and green is the full Chandra band of kev. The green X-ray emission at the edge of the SNR shows the location of the FS and the blue ring inside the SNR shows the RS. The data to produce this image were provided by Vladimír Domček Basic structure of a PWN assuming spherical symmetry. This is an oversimplification as the outer edge of the PWN is confined by the ISM or the SNR it is surrounded by, and the ISM/SNR is not homogeneous. The overall structure of a PWN is typically much more complicated HBA SB160 with a central frequency of MHz and bandwidth of MHz. The top image has been averaged and demixed but not calibrated. The centre image has undergone direction independent calibration as part of the MSSS calibration pipeline The bottom image has been calibrated using the Pre-Facet-Cal (prefactor) pipeline. The synthesised beam is shown in the bottom left corner of each panel. In the bottom panel the synthesised beam is too small to see Clock-TEC solutions of the HBA observations produced when running prefactor. The colours indicate the different LOFAR stations. The y-axis indicates the ionospheric variations. Time is in units of time steps in the MSs HBA SB000 after direction-independent MSSS pipeline calibration in the LTA. SB000 has a central frequency of MHz and bandwidth of MHz. The synthesised beam is shown in the bottom left corner HBA SB160 that has been calibrated using top: direction-independent, time-independent calibration with zero-phase=f and bottom: direction-independent, time-independent calibration with zero-phase=t. SB160 has a central frequency of MHz and bandwidth of MHz. The synthesised beams are shown in the bottom left corner of each panel Comparison of clean models without multiscale (top panel) and with multiscale (bottom panel). Both of these cleans were performed on the same HBA SB with a central frequency of MHz. The image colour scale has been saturated at 50 mjy beam 1. The synthesised beams are shown in the bottom left of each panel The PSFs of a multi-frequency clean of an HBA observation with a central frequency of 144 MHz. The panels have Briggs weightings from left to right of 1.0, 0.0, and 1.0. The synthesised beams of the cleans are shown in the bottom left corner of each panel Comparison of a clean without a uv-cut (top panel) and with a uv-cut of 100 λ (bottom panel). Both of these cleans were performed on the same HBA SB with a central frequency of MHz. The synthesised beams are shown in the bottom left corner of each panel.. 16 vi

12 List of Figures vii 2.8 Radio spectra of isolated point sources in the LOFAR HBA FoV using two different Briggs weightings and using averaged FITS files. Flux density measurements from VLSSR, TGSS ADR and WSRT are also plotted. These point sources have J2000 coordinates in degrees of (290.7, 18.8), (290.3, 17.0), and (293.4, 21.6) respectively Cambridge 5-km telescope observation of PWN G by Green (1985). This observation has a resolution of 7 20 and an observing frequency of 2.7 GHz Low-frequency spectral energy distribution of PWN G produced by Velusamy & Becker (1988). The power law has a spectral index of α 0.13 confirming that PWN G has a spectrum similar to that of the Crab Nebula Chandra ACIS-S X-ray ( kev) intensity map of PWN G (Lu et al., 2002). The intensity is plotted from to counts cm 2 s 1 arcmin 2. The inset image shows a closer view of the central region of the PWN containing the point source that is the central pulsar and the torus of the wind termination shock A schematic diagram of the basic structure of a shell-type SNR A three-colour image of PWN G Chandra X-ray observations are in blue, Mercator Telescope (MAIA) 890 nm optical observations are shown in green and Spitzer (MIPS- GAL) 24 µm IR observations are shown in red. We acquired the MAIA data while visiting the Mercator Observatory as part of the Observation Project course at the University of Amsterdam VLA 1.4 GHz observation of PWN G (Lang et al., 2010). The possible shell is circled in red and PWN G is the bright central source LOFAR HBA MFS image of PWN G and the surrounding environment. The white circles are known SNRs from Green s SNR catalogue (Green, 2014) and the dashed white circles are SNR candidates from Anderson et al. (2017). The red, green and yellow circles are known, group and radio quiet HII regions respectively from the WISE HII region catalogue (Anderson et al., 2014). The synthesised beam is shown in the bottom right corner SED of PWN G from Gelfand et al. (2015). The orange points are VLA fluxes. The green line is the Chandra X-ray integrated flux. The blue points are VERITAS highenergy gamma-ray measurements. The black line is a fit based on their PWN evolution model SED of PWN G using archival observations discussed in Section Note that the IR points measure the flux density of the SN ejecta dust around PWN G , not the PWN itself Radio SED of PWN G using archival radio observations discussed in Section The power laws are a result of fitting a power law or broken power law to the data, they are not a result of the PWN evolution model by Gelfand et al. (2015). The VLA power law is a simple power law fit to only the VLA observations. It has a spectral index of α = The VLA+TGSS broken power law is a broken power law fit to the VLA and TGSS points. It has spectral indices of α 1 = 0.03 and α 2 = The archival radio power law is a simple power law fit to all the radio points (excluding the LOFAR points). It has a spectral index of α = 0.13, the same spectral index found by Velusamy & Becker (1988) (see Sec ) Low-frequency radio SED of PWN G showing the TGSS ADR, VLSSR, and WSRT points and the LOFAR HBA points using Briggs 1.0, Briggs 0.6, and Briggs 1.0 averaged/combined images. The background brightness temperature is also plotted. In the two lowest frequency HBA SBs confusion with the HII region surrounding PWN G results in higher flux density measurements

13 List of Figures viii 4.1 Observations of the Galactic Plane FoV where the LOFAR HBA observation, VGPS mosaic and WSRT mosaic coincide. Top: VGPS 1.4 GHz mosaic. Centre: WSRT 327 MHz mosaic. Bottom: Spitzer 24.0 µm MIPSGAL mosaic Observations of the Galactic Plane at 1.4 GHz (blue, VLA), 327 MHz (green, WSRT) and 144 MHz (red, LOFAR). The synthesized beam sizes are shown in the bottom left corner. Known SNRs from Green s SNR catalogue (Green, 2014) are circled in solid white and candidate SNRs from Anderson et al. (2017) are circled in dashed white. The red, green, yellow, and cyan circles are known, group, radio quiet and candidate HII regions respectively (Anderson et al., 2014) LOFAR HBA observations of G , PWN G , and SNR HC 40. Both panels have a frequency bandwidth of 1.95 MHz. The left panel has a central frequency of MHz and the right panel has a central frequency of MHz. The synthesised beam sizes are shown in the bottom right corner of both panels Observations of G at (a) 1.4 GHz using the VLA, (b) 24.0 µm using Spitzer and (c) X-rays using XMM-Newton. The dashed cyan contours are from LOFAR HBA MHz data (contour levels: 0, 0.25, 0.5, 0.75, 1.0 Jy beam 1 ) and the solid yellow contours are from VLA 1.4 GHz data (contour levels: 12, 14, 16, 18, 20, 22, 24 mjy beam 1 ) from the image in (a) Standard Presto diagnostic plot of the test pulsar PSR J at MHz. This shows the pulse profile as a function of time and frequency during the course of this observation Left: X-ray spectrum showing the best-fit model. The Al Kα instrumental background line around 1.49 kev has been blanked out. Right: Contour plots of the ionization age and post-shock temperature SNR candidates from Anderson et al. (2017) in the FoV. In each row the left panel is the VGPS 1.4 GHz observation, the center panel is the WSRT GHz observation, and the right panel is the LOFAR GHz observation. From top to bottom the rows are the SNR candidates (circled in dashed white) from Anderson et al. (2017): G , G , and G The solid yellow circles are HII regions from the WISE HII catalog (Anderson et al., 2014). The synthesised beams are shown in the bottom left corner of each image SNR candidates from Anderson et al. (2017) in the FoV. In each row the left panel is the VGPS 1.4 GHz observation, the center panel is the WSRT GHz observation, and the right panel is the LOFAR GHz observation. From top to bottom the rows are the SNR candidates (circled in dashed white) from Anderson et al. (2017): G and G The solid yellow circles are HII regions from the WISE HII catalogue (Anderson et al., 2014). The synthesised beams are shown in the bottom left corner of each image Word Cloud summary of the most common words in this thesis C.1 A three-colour image of the Galactic Plane showing G (circled in dashed white), SNR HC 40 and PWN G (both circled in white). The three colours in this image are: LOFAR HBA 150 MHz in red, Westerbork Synthesis Radio Telescope (WSRT) 327 MHz in green and Very Large Array (VLA) Galactic Plane Survey (VGPS) 1400 MHz in blue. The red, yellow and green circles are known, radio quiet and group HII regions respectively from the WISE catalogue (Anderson et al., 2014)

14 List of Tables 2.1 Observation information for both of the calibrator scans and the target scan. All observations were taken on 2015/06/ Archival radio flux density measurements of PWN G Archival X-ray observations of PWN G Archival IR flux density measurements of PWN G Archival gamma-ray observations of PWN G The observed parameters of PSR J The Lu et al. (2007) values are from the 12 th September 2002 Rossi X-Ray Timing Explorer observations The derived properties of PSR J by Camilo et al. (2002) Observed epochs of J from Lu et al. (2007). Periods denoted by a * were taken from Camilo et al. (2002) Details of the pointings for the Arecibo pulsar search of G The expected sensitivity is quoted for a minimum signal-to-noise ratio of 15 and pulse duty cycle of 10% The XMM-Newton best-fit model results. The abundances are provided in Solar units Flux densities of SNR candidates at 1.4 GHz from Anderson et al. (2017) and 327 MHz measured using WSRT observations (Taylor et al., 1996). The WSRT errors are 3σ statistical errors based on the RMS noise in the image; these errors do not take other sources of error, such as confusion, into account ix

15 Abbreviations 3FGL ACIS-S ADR ALFA ASCA SIS ASTRON Cas A CCD CCSN(e) clock-tec CSV DM Einstein IPC FC FITS file FoV FS FWHM GMRT HBA HEGRA HESSCAT INTEGRAL IR IRAS Third Fermi-LAT Catalogue of High-Energy Sources Advanced CCD for Imaging Spectrometer Alternative Data Release Arecibo L-band Feed Array Advanced Satellite for Cosmology and Astrophysics Solid-state Imaging Spectrometer The Netherlands Institute for Radio Astronomy Cassiopeia A Charge-Coupled Device Core Collapse SuperNova(e) clock-total Electron Count Comma Separated Values Dispersion Measure Einstein Image Proportional Counter Filled-Centre Flexible Image Transport System file Field of View Forward Shock Full Width Half Maximum Giant Metrewave Radio Telescope High-Band Array High Energy Gamma-Ray Astronomy High Energy Stereoscopic System CATalogue INTErnational Gamma-Ray Astrophysics Laboratory InfraRed The InfraRed Astronomical Satellite x

16 Abbreviations xi IRC IRIS ISM Jy LBA LOFAR LTA MFS MIPSGAL MJD MoM MS MSSS NE2001 NIR NIRC OSRT PACS PALFA PANIC PSF PSR PWN(e) pybdsf RFI RMS ROSAT RS RXTE SB(s) SED SN(e) SNR Infra-Red Camera Improved Reprocessing of the IRAS Survey InterStellar Medium Jansky Low-Band Array The Low Frequency Array Long Term Archive Multi-Frequency Multiband Infrared Photometer for Spitzer GALactic Plane Survey Modified Julian Date Management of Measurements Measurements Set Multifrequency Snapshot Sky Survey Cordes-Lazio NE2001 Galactic Free Electron Density Model Near Infra-Red Near Infra-Red Camera Ooty Synthesis Radio Telescope Photoconductor Array Camera and Spectrometer Arecibo Pulsar survey using ALFA Persson s Auxilliary Nasmyth IR Camera Point Spread Function Pulsar Pulsar Wind Nebula(e) python Blob Detector and Source Finder Radio Frequency Interference Root Mean Squared RÖntgensatellit Reverse Shock Rossi X-ray Timing Explorer SubBand(s) Spectral Energy Distribution Supernova(e) Supernova Remnant

17 Abbreviations xii SPIRE TGSS THOR VERITAS VLA VGPS VLSSR WISE WSRT XMM YSO Spectral and Photometric Imaging Reciever TIFR GMRT Sky Survey The HI, OH, Recombination Line Survey of the Milky Way Very Energetic Radiation Imaging Telescope Array System Very Large Array VLA Galactic Plane Survey VLA Low-Frequency Sky Survey Redux Source Catalogue Wide-Field Infrared Survey Explorer satellite Westerbork Synthesis Radio Telescope X-ray Multi-Mirror Mission Young Stellar Object

18 Chapter 1 Introduction The Galactic Plane of the Milky Way, as seen at low radio frequencies, is rich in point sources and extended emission including pulsars, pulsar wind nebulae (PWNe), supernova remnants (SNRs) and HII regions. Observing these objects at low frequencies with a wide frequency bandwidth and a large field of view (FoV) can be useful for investigating the characteristics of these objects and can be used to differentiate between objects with similar morphologies, like SNRs and HII regions. LOFAR is the LOw Frequency ARray radio interferometer (van Haarlem et al., 2013). It has Core and Remote stations in the Netherlands and International stations spread across Europe. Including the core and remote stations in the Netherlands means that LOFAR has baselines up to 100 km. Using the 100 km baselines results in an optimum resolution of 3.3. Because of its range of baselines, LOFAR is an excellent instrument for observing both extended emission and point sources with good resolution. LOFAR consists of two antenna arrays: the Low Band Antennas (LBA, shown in Fig. 1.1) and High Band Antennas (HBA). The LBA observes between 10 and 90 MHz while the HBA observes between 110 and 250 MHz. The low observing frequencies, wide bandwidth, and large FoV of the LOFAR instrument make it ideal for observing and discovering steep-spectra objects and for differentiating between SNRs and HII regions. Observing the Galactic Plane at low frequencies with a wide FoV presents unique interferometry challenges, particularly regarding ionospheric issues, calibration, and dealing with the extended emission that dominates the Galactic Plane. We present a detailed investigation of methods for processing and analysing LOFAR HBA imaging observations of the Galactic Plane in Chapter 2. We discuss four methods of HBA calibration (Sec. 2.2), producing HBA images using WSClean (Sec. 2.3), and measuring point source flux densities with pybdsf (Sec. 2.4). In this thesis we will present the analysis of LOFAR observations of the Galactic Plane. We will focus on investigating PWN G and on SNRs and SNR candidates in the FoV. 1

19 Chapter 1: Introduction 2 Figure 1.1: LOFAR low band antennas on the superterp located in Drenthe, the Netherlands. 1.1 Supernova remnants While the extended emission in the Galactic Plane presents challenges for low-frequency interferometry, some low frequency extended emission is interesting and exciting to observe; for example SNRs. At the end of a massive ( 8 M ) star s life, photo-disintegration occurs and the star can no longer support itself through degeneracy pressure. This causes the star to collapse and subsequently explode as a core-collapse supernova (CCSN; Carroll & Ostlie, 2006). The explosion produces several solar masses of ejecta that expands into the surrounding medium at speeds of tens of thousands of kilometers a second (Slane, 2017). At the interface of the expanding ejecta and the surrounding material a forward shock (FS) forms. As the FS decelerates a reverse shock (RS) forms that heats cold supernova ejecta dust that is also formed in the explosion. At the FS the material is compressed by a factor of 4. As the shock is expanding, the material inside is diluted. This compressed material with dilute material inside gives rise to the shellmorphology usually expected of SNRs (Vink, 2012a). We further discuss CCSNe and the different types of SNRs - shell-type, filled-centre, and composite - in Section 3.2. A classic example of an SNR with a shell morphology and both a FS and RS is Cassiopeia A, shown in Figure 1.2. SNRs have spectral indices of α 0.5, where S ν ν α for S ν flux density in Jy and ν frequency in Hz (Onić, 2013). This negative spectral index is because SNRs emit synchrotron radiation. The FS of the SNR accelerates particles to relativistic energies and the particles emit synchrotron radiation as they spiral around magnetic field lines. Having a negative spectral index means that SNRs become brighter at lower frequencies and can be observed and detected using low-frequency observations with wide bandwidth.

20 Chapter 1: Introduction 3 Figure 1.2: A three colour image of shell-type SNR Cassiopeia A. Red is a high-resolution VLA observation at 4.7 GHz. Green and blue are both Chandra X-ray observations. Blue shows kev X-rays showing the synchrotron continuum and green is the full Chandra band of kev. The green X-ray emission at the edge of the SNR shows the location of the FS and the blue ring inside the SNR shows the RS. The data to produce this image were provided by Vladimír Domček. There is another common source of extended emission in the Galactic Plane: HII regions. It can be difficult to differentiate between SNRs and HII regions as SNRs and HII regions can be morphologically similar. An HII region is a region of atomic hydrogen which is usually ionised due to heating by stars in the region. Typically, HII regions are star-forming regions and they can be compact or extended, morphologically irregular or bubble-like. The thermal emission from HII regions is caused by free-free radiation which results in a spectral index of α 0. This means that HII regions are brighter at higher frequencies. As HII regions become brighter at higher frequencies while SNRs become brighter at lower frequencies lowfrequency, wide-bandwidth observations are excellent for differentiating between HII regions and SNRs. We use this concept in Section to determine that two new Galactic SNR candidates are in fact much more likely to be HII regions while one is a good SNR candidate, and to show in Section that there

21 Chapter 1: Introduction 4 is no SNR shell around PWN G Pulsars CCSN explosions result in a shell of expanding ejecta, FS, RS and cold ejecta dust. The cold ejecta dust is heated by the RS. CCSNe also produce a central compact object: a neutron star or a black hole. Often what is left is a rapidly spinning neutron star with a strong magnetic field: a pulsar. A pulsar is a rapidly rotating neutron star with a strong dipole magnetic field (Carroll & Ostlie, 2006). This makes pulsars like flashing astronomical lighthouses. By studying the pulsed light from a pulsar properties of the pulsar can be found. Dispersion measure (DM) is the column density of electrons between the pulsar and the observer. The DM of a pulsar in combination with a model of the distribution of free electrons in the Galaxy (eg. Cordes & Lazio 2002) can be used as a proxy for distance to the pulsar and SNR. The derivative of the spin period of a pulsar can be used to find an approximate age, called the characteristic age (τ c ), of the pulsar, and hence the SNR. The equation for the characteristic age of a pulsar is given by: τ c P 2 P where P is the pulse period and P is the period derivative or spin-down rate. This equation assumes that the initial period (P 0 ) of the pulsar is much smaller than the current period (P ) and that the spin-down is due to magnetic dipole radiation (Lorimer & Kramer, 2004). It has been shown that the characteristic age can be inaccurate for pulsars with ages more accurately known through other methods. As such, the characteristic age should be used with care and should be considered an approximation only. We discuss some basic properties of the pulsar in PWN G in Section (1.1) 1.3 Pulsar wind nebulae A pulsar wind is the highly magnetised, relativistic outflow of particles from a neutron star magnetosphere. This wind expands into the interstellar medium (ISM) or the SNR shell surrounding the pulsar to produce a PWN. As the wind expands into the PWN, which is now expanding slowly as it is confined by the ISM or SNR, this produces a termination shock. This is where the ram pressure of the unshocked wind balances with the pressure of the PWN (Slane, 2017). PWNe are structured as shown in Figure 1.3. The main focus of this Master s research is a specific PWN, PWN G We present an in-depth investigation of PWN G in Chapter 3. Observations and modelling of PWNe can help answer fundamental physics and astronomy questions (Gelfand et al., 2015). By investigating the spectral energy distribution (SED) of a PWN the multiplicity, or number of particles produced in the magnetosphere, of the neutron star and the acceleration of particles

22 Chapter 1: Introduction 5 Figure 1.3: Basic structure of a PWN assuming spherical symmetry. This is an oversimplification as the outer edge of the PWN is confined by the ISM or the SNR it is surrounded by, and the ISM/SNR is not homogeneous. The overall structure of a PWN is typically much more complicated. can be investigated. Modelling the evolution of a PWN can provide information about the CCSN that formed the pulsar such as the initial energy of the SN and the mass of the progenitor star. PWN evolution can be used to investigate the initial properties of the pulsar itself, such as the initial spin period of the pulsar. The low-frequency flux density of a PWN is important because the lowest frequency emitting electrons can act as a calorimeter for the total particle energy of the PWN. The low-frequency flux density can be used to test current evolution and SED models, for example by finding the cut-off energy (Gelfand et al., 2015). We investigate the SED of PWN G in Section 3.3.

23 Chapter 2 LOFAR Observations and Processing The LOFAR observations analysed for this project were centred on PWN G and were taken in observing Cycle 4 as part of project LC4 011 on 2015/06/12. Both HBA (ObsID: ) and LBA (ObsID: ) observations were acquired, but this project is focused on the HBA observations. This is due to the extra calibration and processing required to perform LBA analysis. 2.1 HBA observations Our HBA target and calibrator scans cover the frequency range from MHz to MHz. The observing bandwidth was split into 260, khz wide subbands (SBs). For these observations an 18 min calibrator scan of 3C380 was taken both before and after the 3 hr target scan. Details of the observations are shown in Table 2.1. Despite the information in the LOFAR Management of Measurements (MoM) tool it was difficult to ascertain what processing had been performed on the observations in the automatic pipeline. In this chapter we investigate and compare four different methods for calibrating LOFAR HBA observations (Sec. 2.2), and we discuss imaging with WSClean (Sec. 2.3), and measuring flux densities with pybdsf (Sec. 2.4). The LOFAR observations were flagged to remove radio frequency interference (RFI), demixed, and averaged as part of standard LOFAR pre-processing. Demixing involves removing the effects of the very bright radio Scan type Scan code Central source Right ascension Declination Start time (UTC) End time (UTC) Duration (seconds) Calibrator L C380 18h29m31.8s +48d44m46s 00:18: :32: Target L G h30m31.0s +18d52m46s 00:34: :04: Calibrator L C380 18h29m31.8s +48d44m46s 03:05: :19: Table 2.1: Observation information for both of the calibrator scans and the target scan. All observations were taken on 2015/06/12. 6

24 Chapter 2: LOFAR Observations and Processing 7 sources, Cassiopeia A and Cygnus A, that affect LOFAR images even when they are far from the phase centre of the FoV. The data were averaged from 64 channels per SB to 4 frequency channels per SB. The LOFAR synthesized beam size is at MHz using a Briggs weight of 1.0. Different weighting schemes and their effect on the synthesised beam are discussed in Section 2.3. The calibrator data were run through the calibrator pipeline, which performs flagging, demixing, and averaging as well as finding gain calibration solutions. The target data were run through the Multifrequency Snapshot Sky Survey (MSSS, Heald et al., 2015) imaging pipeline (see Sec ), where the data were calibrated using a direction-independent method. This is where the calibration solutions from the calibrator scan are directly applied to the target scan. A single SB uncalibrated clean image and the same SB after MSSS calibration are shown in Figure 2.1. Ionospheric variations during the observations were particularly pronounced. This is clear from the clock- TEC plots produced by prefactor 1 (see Sec ), where TEC is the total electron content of the ionosphere. The LOFAR core stations operate on the same clock, but the remote stations have their own clocks. This means that the relative drift between the core and remote station clocks needs to be corrected. The clock-tec solutions of the HBA observations are shown in Figure 2.2. The solutions should be smooth and clustered around 0. In contrast, one can see in Figure 2.2 that the solutions are extremely noisy and have large variations. The ionospheric conditions during these observations thus likely caused some of the calibration problems discussed in Section Calibration methods Calibrating radio interferometric data means calibrating the phase and amplitude of the signal as seen by the individual elements in the array (stations in the case of LOFAR). Phase errors occur due to e.g. the station clocks and the ionosphere and result in deconvolution artefacts and an overall increase in noise in the image (van Weeren et al., 2016). Ionospheric phase errors are caused by the column density of electrons in the ionosphere (TEC), which varies in time and is different along different lines of sight and for different stations. LOFAR is a geographically distributed array, which means that phase errors need to be corrected per station. Each station also has a different line of sight and hence experiences different ionospheric variations. This makes phase-calibrating LOFAR observations a challenge. Amplitude calibration is essential because the measured flux density depends on the telescope s response, which varies with time and pointing direction. Correcting for amplitude means that flux densities measured in the final image are accurate. 1

25 Chapter 2: LOFAR Observations and Processing 8 Figure 2.1: HBA SB160 with a central frequency of MHz and bandwidth of MHz. The top image has been averaged and demixed but not calibrated. The centre image has undergone direction independent calibration as part of the MSSS calibration pipeline The bottom image has been calibrated using the Pre-Facet-Cal (prefactor) pipeline. The synthesised beam is shown in the bottom left corner of each panel. In the bottom panel the synthesised beam is too small to see.

26 Chapter 2: LOFAR Observations and Processing 9 Figure 2.2: Clock-TEC solutions of the HBA observations produced when running prefactor. The colours indicate the different LOFAR stations. The y-axis indicates the ionospheric variations. Time is in units of time steps in the MSs Pre-Facet-Cal Pre-Facet-Calibration, or prefactor, is a calibration method specifically designed and developed to overcome the unique challenges of calibrating LOFAR HBA observations such as the ionospheric variations across the wide FoV, the clock drift between stations, and the different lines of sight (and hence different ionospheres) of the different stations. Here we will briefly discuss the steps of prefactor; more detail can be found in van Weeren et al. (2016). The first steps in prefactor involve direction-independent correction. This means removal of RFI, removal of off-axis bright sources (demixing), averaging, solving for the calibrator complex gains, clock-tec separation on the calibrator, transfer of amplitudes and clocks from the calibrator to the target and mediumresolution target field amplitude and phase calibration. RFI removal is performed by AOFlagger (Offringa, 2010). The final calibration on the target amplitude and phase is a self-calibration performed on groups of combined SBs (usually 10 per group).

27 Chapter 2: LOFAR Observations and Processing 10 The prefactor pipeline can be found on the LOFAR GitHub 2. To run prefactor the user needs to find or produce the appropriate SkyModel files for their calibrator object and the target field. A SkyModel is a file that contains basic information (eg. right ascension, declination, semi-major and semi-minor axis, orientation, and flux density) about known sources in the field of interest. The user needs to edit the Pre-Facet-Cal parset and the config file to match their MSs and their computer or cluster specifications. Prefactor can then be run using a command similar to: genericpipeline.py Pre-Facet-Cal.parset -c pipeline.cfg -d which was the command we used to run prefactor on the Dragnet 3 cluster Direction-independent calibration An alternative calibration method is a custom direction-independent calibration. The steps are: Flagging (both the calibrator scan and target scan): Flagging the ears Auto flagging of RFI with AOFlagger (Offringa, 2010) Finding the calibrator gaincal solutions Applying the calibration solutions from the calibrator scan to the target scan Flagging the target scan again using AOFlagger It is important to flag the ears (HBA Core stations are split into HBA0 and HBA1 ears, each with 24 HBA tiles, and these have very short baselines between them) to remove the largest scale emission that causes a large-scale wave pattern in the image, an example of which is shown in figure 2.1 (centre panel), and produces an incorrect clean model. The gain calibration solution was found using a solution interval of 2. This means that a gaincal solution is found for every two time intervals. The calibration solutions are then exported using parmexportcal using zerophase=t or zerophase=f. Using parmexportcal automatically produces time-independent gain solutions. Setting zerophase=t sets the phase solution to zero. For most observations it is more appropriate to set zerophase=f. However, setting zerophase=t is useful to reduce the impact of poor ionospheric conditions. The calibration solutions are then applied to the flagged target measurement set Calibrating the G observation As described above, we considered four possible calibration methods for our LOFAR observations:

28 Chapter 2: LOFAR Observations and Processing 11 Figure 2.3: HBA SB000 after direction-independent MSSS pipeline calibration in the LTA. SB000 has a central frequency of MHz and bandwidth of MHz. The synthesised beam is shown in the bottom left corner. MSSS imaging pipeline prefactor direction-independent, time-dependent, non-zero-phase calibration direction-independent, time-dependent, zero-phase calibration we will now compare each of these strategies in order to come to a conclusion on which methods deliver the best-possible calibration of our data The MSSS imaging pipeline The data products available on the Long Term Archive (LTA) for this field were the averaged and demixed observations, as well as the averaged and demixed observations processed with the MSSS imaging pipeline. The MSSS imaging pipeline is the calibration pipeline used to calibrate observations for imaging as part of the MSSS survey. The MSSS imaging pipeline uses iterations of direction-independent calibration and self-calibration (Heald et al., 2015). Figure 2.1 (centre panel) shows an example of a SB processed using the MSSS imaging pipeline. It appears that the pipeline does a good job of calibration. However, Figure 2.3 shows that the pipeline does not achieve good results for all SBs. The MSSS imaging pipeline calibrates the most sensitive SBs well but does not produce reasonable results at the edges of the HBA frequency range.

29 Chapter 2: LOFAR Observations and Processing Pre-Facet-Cal As the ionospheric conditions were poor during these observations, prefactor attempted to calibrate using the clock-tec solution shown in Figure 2.2. This resulted in prefactor producing poor calibration solutions. The result of the prefactor calibration pipeline on the PWN G FoV is shown in Figure 2.1 (bottom panel) Direction-independent calibration An example of an SB in our FoV with a direction-independent, time-dependent and non-zero-phase calibration compared to the same SB with a direction-independent, time-dependent and zero-phase calibration is shown in Figure 2.4. It is clear that the zero-phase calibration greatly reduces the large-scale noise patterns visible in the non-zero-phase calibration Chosen calibration method We found that the calibration method that produced the best results for all SBs was the directionindependent, time-independent, zero-phase method. This method was selected for a combination of reasons. The sources in the field have realistic flux densities with maximum flux densities of point sources up to a few Janksy compared to hundreds of Janksy in some SBs from the MSSS imaging pipeline. The objects in the FoV have morphologies as expected from multi-frequency observations, such as with the Very Large Array and the Westerbork Synthesis Radio Telescope (Fig. 4.1), unlike the images produced by the MSSS imaging pipeline (Fig. 2.3) and the prefactor pipeline (Fig. 2.1). The images do not suffer from large scale noise-patterns like the noise in the direction-independent, time-dependent, zero-phase method (Fig. 2.4). Using a time-independent, zero-phase calibration is unusual but in this case using this method reduces the calibration problems caused by the poor ionospheric conditions. As such this was the method we used to process the LOFAR observations of the PWN G field. A non-standard or custom calibration, such as a direction-independent, time-independent, zero-phase method, can be performed using the LOFAR specific software DPPP (Default Preprocessing Pipeline 4 ) and LOFAR specific instruction files called parsets. These instruction files require parameters such as msin and msout to define the MS (or MSs) to perform the function on and the name of the output MS (or MSs). It then requires a steps parameter which is a list describing the functions to be performed in the order they should be performed. For example: steps=[aoflagger, gaincal] would first flag RFI using AOFlagger and then perform gain calibration. Additional requirements for the steps can then be added to the end of the parset. For example if one of the steps is flag then the user can define specific baselines to flag, such as the ears. 4

30 Chapter 2: LOFAR Observations and Processing 13 Figure 2.4: HBA SB160 that has been calibrated using top: direction-independent, time-independent calibration with zero-phase=f and bottom: direction-independent, time-independent calibration with zerophase=t. SB160 has a central frequency of MHz and bandwidth of MHz. The synthesised beams are shown in the bottom left corner of each panel. To perform the full calibration pipeline we used Python functions. Our pipeline first writes the required parsets and then executes them with DPPP in the correct order. At each step the pipeline writes new MSs instead of over-writing the original MSs. This is to prevent the calibration from altering the original data so that multiple calibration methods could be tested without re-downloading the data. This vastly increases the computer storage required to run the pipeline as each MS is typically on the order of Gigabytes and the pipeline runs on 260 target MSs and 260 calibrator MSs, but the extra MSs are deleted after the pipeline is completed. We produce basic cleans (using WSClean, see Sec. 2.3) of the MSs produced at each step in order to make de-bugging simpler and to enable the user to check the products easily by eye at each step. The functions used to perform the calibration and the function to run the full pipeline are shown in Appendix A. The individual SBs were then combined into sets of ten into new MSs. Two rounds of self-calibration were

31 Chapter 2: LOFAR Observations and Processing 14 performed as the final calibration steps, the first round based on a model from the TGSS ADR 5 catalogue (Intema et al., 2017). 2.3 Imaging LOFAR imaging is performed using the WSClean tool (Offringa et al., 2014). WSClean uses a w-stacking clean algorithm to image interferometric radio data. WSClean has a wide range of parameters and options, here we will only discuss those used for the analysis in this work. The basic WSClean parameters are scale, size, trim, niter and threshold. The scale parameter sets the size of the pixels in arcseconds or arcminutes. The size parameter sets the height and width of the image in pixels during cleaning which can then be trimmed to a height and width set by the trim parameter. The niter parameter stands for number of iterations and defines the number of major clean cycles. As the name suggests, the threshold parameter sets a threshold for how deep the clean is. If both niter and threshold are used then the clean will stop when the maximum number of major clean iterations is reached or when the threshold is reached, whichever is reached first. The multiscale parameter is useful for observations containing extended emission. The difference between a clean without multiscale and a clean with multiscale is shown in Figure 2.5. A clean without multiscale uses simple delta functions to model the emission. Including the multiscale parameter means that WSClean uses delta functions and Gaussian functions to model the emission. This produces a much more accurate model. In WSClean the weight parameter can be used to vary the weighting of different baselines. This is done using Briggs weighting, where a Briggs weight of 1.0 down weights the short baselines and up-weights the long baselines while a Briggs weight of 1.0 does the opposite. This means that a Briggs weight of 1.0 increases the resolution of an image, but increases the noise. Briggs 1.0 also resolves out extended emission. Briggs 1.0 decreases the resolution and noise of an image, and is better for observing extended emission. As we are interested in extended emission more than point source resolution, we opt for a Briggs weight of 1.0. The point spread functions (PSFs) of the same LOFAR image with three different Briggs weightings are shown in Figure 2.6. In an observation where extended emission dominates the short baselines can produce large scale noise. To prevent this it is possible to cut the shortest baselines during the clean using minuv-l. This can be used to remove the shortest baselines, and hence the largest scale emission, but also removes a large amount of noise and allows a deeper clean. The difference between a clean with a uv-cut and a clean without a uv-cut is shown in Figure 2.7. We use a uv-cut of 100 λ (where λ is wavelength) in all of our cleans to remove the large scale noise and achieve deeper cleans. Using this uv-cut the largest scale emission we can detect is TIFR GMRT Sky Survey Alternative Data Release,

32 Chapter 2: LOFAR Observations and Processing 15 Figure 2.5: Comparison of clean models without multiscale (top panel) and with multiscale (bottom panel). Both of these cleans were performed on the same HBA SB with a central frequency of MHz. The image colour scale has been saturated at 50 mjy beam 1. The synthesised beams are shown in the bottom left of each panel. Figure 2.6: The PSFs of a multi-frequency clean of an HBA observation with a central frequency of 144 MHz. The panels have Briggs weightings from left to right of 1.0, 0.0, and 1.0. The synthesised beams of the cleans are shown in the bottom left corner of each panel.

33 Chapter 2: LOFAR Observations and Processing 16 Figure 2.7: Comparison of a clean without a uv-cut (top panel) and with a uv-cut of 100 λ (bottom panel). Both of these cleans were performed on the same HBA SB with a central frequency of MHz. The synthesised beams are shown in the bottom left corner of each panel. 2.4 Flux determination The point source flux densities were measured using pybdsf 6. pybdsf is a LOFAR-specific program developed at the Netherlands Institute for Radio Astronomy (ASTRON) that can be used to measure the flux densities of sources in Flexible Image Transport (FITS) files. In order to check the flux densities in the FITS files we produced from the LOFAR HBA observations we used pybdsf to measure the flux densities of point sources and compared the flux densities to those measured by the TGSS ADR, VLSSR 7, and WSRT 8 point source databases. We wrote a script to run pybdsf on the FITS files we produced. The WSRT and VLSSR survey catalogues were in fixed-width column files with units and formatting inconsistent with TGSS and pybdsf. We wrote a script to extract and convert the information and rewrite the appropriate data to new comma separated value (CSV) files VLA Low-Frequency Sky Survey Redux Source Catalogue, radio-catalog/vlssr.html 8 Westerbork Synthesis Array Telescope,

34 Chapter 2: LOFAR Observations and Processing 17 We then wrote a script to extract the TGSS ADR flux densities from the database. Finally, we wrote a script to extract the pybdsf measured flux densities from the CSV files produced by pybdsf. We could then compare the point source flux densities from pybdsf to those from TGSS ADR, VLSSR and WSRT. The scripts discussed here can be found in Appendix B. When comparing the point source flux densities we found that pybdsf often measured higher flux densities than expected. Due to the larger beam size of the LOFAR images often pybdsf would be measuring flux densities from multiple point sources where TGSS ADR would be measuring the flux density of one source. We edited our scripts to produce SAODS9 9 region files for the pybdsf point sources and TGSS ADR point sources in order to check that the pybdsf and TGSS ADR point sources matched. We also excluded sources in the pybdsf files that had semi-major axes greater than 0.1 to restrict the results to point-like sources. We excluded any TGSS ADR sources that were within 0.1 of another TGSS ADR source to prevent confusion. When pybdsf finds a point source in different SBs it does not always define the point source as the same size in all of the SBs. If pybdsf places a much larger ellipse around a source in one SB compared to another SB this can dramatically affect the measured flux density. As such, we altered our pybdsf script to use the aperture parameter. Using the aperture parameter we set pybdsf to measure the flux density of a point source only within an aperture of set size centred on the brightest pixel of the point source. We set the aperture radius to be the semi-major axis of the SB s synthesised beam. This significantly reduced the scatter on the flux density measurements. In Figure 2.8 the radio spectra of three points sources in the FoV are shown. The LOFAR HBA flux density measurements are not consistent with the flux density measurements from VLSSR, TGSS ADR and WSRT. We tested the effects of a different Briggs weightings in case this could affect the flux density measurements. Figure 2.8 shows that the Briggs weighting does not strongly affect the flux measurement. We also combined the FITS files into chunks of six images using Astropy 10, but this did not significantly affect the measurements. As the point sources are in the Galactic Plane, the steepening of the spectra may be due to the Galactic synchrotron background. We found the brightness temperature in the directions of the point sources using 408 MHz observations by Haslam (1983). The code to search the Haslam (1983) tables was written by David Gardenier 11. We then converted the brightness temperature into flux density using: S ν = θ λ 2 T B (2.1) where S ν is the flux density in Jy beam 1, θ is the semi-major axis of the synthesised beam in arcseconds, λ is the wavelength in cm and T B is the brightness temperature in Kelvin. Figure 2.8 shows that the

35 Chapter 2: LOFAR Observations and Processing 18 Figure 2.8: Radio spectra of isolated point sources in the LOFAR HBA FoV using two different Briggs weightings and using averaged FITS files. Flux density measurements from VLSSR, TGSS ADR and WSRT are also plotted. These point sources have J2000 coordinates in degrees of (290.7, 18.8), (290.3, 17.0), and (293.4, 21.6) respectively. background brightness temperature does not contribute significantly to the flux densities of the point sources. It is therefore likely that the steepening effect in the LOFAR measurements is due to an instrumental or calibration effect. A possible cause that we have yet to test is the flux of the very large scale emission being added into the point sources. If the issue, this is likely to have occurred during the calibration. If there is large scale emission that is not accounted for in the sky model then the flux from that emission may be added into the point sources that are in the sky model. This could artificially increase the flux density of the point sources. To mitigate this problem in the calibration step a uv-cut could be applied to resolve out the large scale emission. A uv-cut could be applied that specifically cuts baselines such that the observable largest scale emission is exactly the size of the object of interest, in this case PWN G It would then be important at the clean stage to apply the same uv-cut and ensure that the correct Briggs weighting is applied to produce a synthesised beam approximately the size of PWN G This is future work for this project and could explain the unexpectedly steep spectra of the point sources.

36 Chapter 3 PWN G In this chapter we will discuss PWN G PWN G was the main source of interest that led to the proposal for and acquisition of the LOFAR observations analysed in this project. 3.1 Past work Discovery PWN G , or the Bull s Eye PWN, was first observed by Altenhoff et al. (1979), Downes et al. (1980), and Reich et al. (1984) as a small diameter source in radio Galactic Plane surveys. Green (1985) identified it as a small, diffuse, apparently thermal source, shown in Figure 3.1, and noted that it may be an HII region or a filled-centre SNR. Reich et al. (1985) identified PWN G as a Crab-like, filledcentre SNR with a flat spectrum and significant polarisation. Velusamy et al. (1986) observed the region surrounding PWN G at 612 MHz using the Westerbork Synthesis Radio Telescope (WSRT) and they returned again to the idea that PWN G is an HII region associated with the large HII region centred at G (Anderson et al., 2014). In order to confirm whether PWN G is an HII region or a SNR, Velusamy & Becker (1988) performed high resolution 1.4, 1.6, and 4.8 GHz Very Large Array (VLA) observations and measured the flux density at 327 MHz with the Ooty Synthesis Radio Telescope (OSRT). They also analysed Infrared Astronomical Satellite (IRAS) data. Velusamy & Becker (1988) used flux density measurements from Green (1985), Velusamy et al. (1986), Caswell (1985) and Reich et al. (1985) to complement their flux densities and produce the SED in Figure 3.2. PWN G was confirmed as a Crab-like, filled-centre SNR by Velusamy & Becker (1988) using high resolution VLA and OSRT observations to show that the spectrum is that of a non-thermal synchrotron emitting object. 19

37 Chapter 3: Pulsar Wind Nebula G Figure 3.1: Cambridge 5-km telescope observation of PWN G by Green (1985). This observation has a resolution of 7 20 and an observing frequency of 2.7 GHz Observations Low-frequency observations PWN G was first discovered in the radio and has since been observed by various radio telescopes. Information regarding the observations and flux density measurements in the radio can be found in Table 3.1.

38 Chapter 3: Pulsar Wind Nebula G Figure 3.2: Low-frequency spectral energy distribution of PWN G produced by Velusamy & Becker (1988). The power law has a spectral index of α 0.13 confirming that PWN G has a spectrum similar to that of the Crab Nebula. Source Telescope Frequency (GHz) Flux density (mjy) Altenhoff et al. (1979) Effelsberg Downes et al. (1980) Effelsberg Reich et al. (1984) Effelsberg Green et al. (1985) Cambridge Low-Frequency Synthesis ± 0.15 Telescope Caswell et al. (1985) Penticton Synthesis Telescope Velusamy et al (1986) WSRT ± 30 Velusamy et al (1988) VLA (C-configuration) ± 30 Velusamy et al (1988) VLA(C-configuration) ± 30 Velusamy et al (1988) VLA (C-configuration) ± 20 Velusamy et al (1988) Ooty Synthesis Radio Telescope ± 75 Taylor et al. (1996) WSRT ± 17 Lang et al (2010) VLA ± 30.0 Lang et al (2010) VLA ± 25.0 Lang et al (2010) VLA ± 20.0 Table 3.1: Archival radio flux density measurements of PWN G

39 Chapter 3: Pulsar Wind Nebula G Figure 3.3: Chandra ACIS-S X-ray ( kev) intensity map of PWN G (Lu et al., 2002). The intensity is plotted from to counts cm 2 s 1 arcmin 2. The inset image shows a closer view of the central region of the PWN containing the point source that is the central pulsar and the torus of the wind termination shock X-ray observations Weak X-ray emission from PWN G was detected by Seward (1989) using the Einstein IPC instrument. Lu et al. (2001a) discovered the X-ray jet associated with PWN G using ROSAT and ASCA SIS observations. Lu et al. (2001b) also measured the X-ray flux density of PWN G Chandra observations of PWN G were taken in 2001 (Lu & Wang, 2001) with further details published in 2002 (Lu et al., 2002). One can see the X-ray morphology of PWN G in the intensity map in Figure 3.3. PWN G was observed in the hard X-rays by the INTEGRAL space telescope (Krivonos et al., 2017). A summary of the X-ray observations can be found in Table 3.2.

40 Chapter 3: Pulsar Wind Nebula G Source Telescope Energy (kev) Flux (erg cm 2 s 1 ) Photon index (Γ) Lu at al. (2001) ASCA SIS ± 0.2 Lu et al. (2002) Chandra 2 10 (5.43 ± 0.035) ± 0.01 Krivonos et al (2017) INTEGRAL (0.78 ± 0.10) Bocchino et al (2010) SUZAKU 2 10 (4.7 ± 0.7) Table 3.2: Archival X-ray observations of PWN G Source Telescope Frequency (THz) Flux (Jy) density Extinction corrected flux density (Jy) Taylor et al. (1996) IRAS Taylor et al. (1996) IRAS Taylor et al. (1996) IRAS Taylor et al. (1996) IRAS Temim et al. (2010) IRAC Temim et al. (2010) IRAC Temim et al. (2010) IRAC ± ± 0.7 Temim et al. (2010) IRAC ± 15 Temim et al. (2017) Akari IRC ± 0.26 Temim et al. (2017) SOFIA FORCAST ± 2.4 Temim et al. (2017) Spitzer MIPS ± 2.1 Temim et al. (2017) SOFIA FORCAST ± 2.5 Temim et al. (2017) SOFIA FORCAST ± 8.5 Temim et al. (2017) SOFIA FORCAST ± 8.3 Temim et al. (2017) Herschel PACS ± 11.4 Temim et al. (2017) Herschel PACS ± 13.4 Temim et al. (2017) Herschel PACS ± 14.9 Temim et al. (2017) Herschel SPIRE ± 5.2 Temim et al. (2017) Herschel SPIRE ± 2.8 Temim et al. (2017) Herschel SPIRE ± 1.1 Table 3.3: Archival IR flux density measurements of PWN G Infrared observations As PWN G is in the Galactic Plane it has been observed as part of various IR Galactic Plane surveys. The observations have been summarised in Table 3.3. The IR emission will be further discussed in Section Gamma-ray observations PWN G has been observed at high-energies by the High-Energy-Gamma-Ray Astronomy (HEGRA) telescope and Very Energetic Radiation Imaging Telescope Array System (VERITAS). These observations

41 Chapter 3: Pulsar Wind Nebula G Source Telescope Energy Photon density (GeV) (photons cm 2 s 1 TeV 1 ) Acciari et al. (2010) VERITAS 311 (1.10 ± 0.56) Acciari et al. (2010) VERITAS 492 (4.2 ± 1.4) Acciari et al. (2010) VERITAS 780 (1.12 ± 0.45) Acciari et al. (2010) VERITAS (6.2 ± 1.7) Acciari et al. (2010) VERITAS (3.9 ± 2.1) Table 3.4: Archival gamma-ray observations of PWN G are summarised in Table 3.4. The HEGRA measurement is an upper limit and can be found in Aharonian et al. (2002). There is no associated Fermi source in the Fermi LAT 4-Year Point Source Catalogue The distance to PWN G The distance to PWN G has been approximated using a variety of methods. Lu et al. (2002) estimated a distance of 5 kpc using Galactic absorption. Camilo et al. (2002) used the DM of the central pulsar, PSR J (see Section 3.1.5), and NE2001 (Cordes & Lazio, 2002) to give a distance of 8 kpc. Weisberg et al. (2008) use Arecibo HI absorption measurements to put the pulsar between 3.2 kpc and 10 kpc. Leahy et al. (2008) find a distance of 6.2 ± 0.1 kpc using a morphological association with a CO molecular cloud. Later Lee et al. (2012) discount this association and the distance to PWN G calculated using the CO cloud. Using the photometric distance to the IR-excess objects (discussed in Section ) Kim et al. (2013) find an approximate distance to PWN G of 6.0 ± 0.4 kpc Magnetic Fields and Polarisation Lang et al. (2010) used the VLA to investigate the polarisation and magnetic field properties of PWN G They estimate the upper limit on the magnetic field strength to be 1250 µg PSR J : the central pulsar of PWN G PSR J , the pulsar associated with PWN G , was discovered by Camilo et al. (2002). They found the pulsar using 1180 MHz Arecibo observations and subsequently also found the pulsar in archival ASCA X-ray observations. A summary of the pulsar parameters observed by Camilo et al. (2002) are shown in Table 3.5 while the derived parameters are shown in Table 3.6.

42 Chapter 3: Pulsar Wind Nebula G Parameter Value - Camilo et al. (2002) Value - Lu et al. (2007) Right ascension (J2000) 19 h 30 m s Declination (J2000) Period, P (ms) (9) (4) Period derivative, P (s s 1 ) (1) (6) Epoch (MJD [TBD]) Dispersion measure, DM (cm 3 pc) 308(4) Flux density at 1180 MHz (µjy) 60 ± 10 Flux at 2 10 kev (erg cm 2 s 1 ) (pulsed) Pulse FWHM at 1180 MHz (ms) 15 ± 2 Pulse FWHM at 2 10 kev (ms) 25 Table 3.5: The observed parameters of PSR J The Lu et al. (2007) values are from the 12 th September 2002 Rossi X-Ray Timing Explorer observations. Parameter Value Distance, d (kpc) 8 Characteristic age, τ c (yr) 2900 Spin-down luminosity, Ė (erg s 1 ) Magnetic field strength, B (G) Pseudo luminosity at 1400 MHz, 1 (mjy kpc 2 ) Table 3.6: The derived properties of PSR J by Camilo et al. (2002). Kaplan & Moon (2006) performed a search for near-infrared (NIR) counterparts to young pulsars using Persson s Auxiliary Nasmyth Infrared Camera (PANIC) on the Magellan I (Baade) telescope and the Near-IR Camera (NIRC) on the 10 m Keck I telescope. They did not detect a NIR counterpart to PSR J but could define the upper limits: J band : < 0.17 mjy (3.1) K band : < 0.03 MJy (3.2) Ilardo (2006) searched for, but did not detect, giant pulses from PSR J Chevalier (2005) calculated the initial spin period of the pulsar to be 100 ms with a surface magnetic field of G. X-ray timing of PSR J using Rossi X-Ray Timing Explorer observations (RXTE) were performed by Lu et al. (2007). They found a best-fit constant spin-down rate of P = (6) s s 1. However, they note that there is evolution in the period, shown in Table 3.7, suggesting strong timing noise or glitch activity.

43 Chapter 3: Pulsar Wind Nebula G Date (UT) Epoch (MJD [TBD]) Period (s) (5)* (9)* (4) (3) (5) Table 3.7: Observed epochs of J from Lu et al. (2007). Periods denoted by a * were taken from Camilo et al. (2002). Figure 3.4: A schematic diagram of the basic structure of a shell-type SNR. 3.2 Supernova remnant features of PWN G Theoretical predictions suggest that CCSNe can efficiently produce between 0.1 and 1.0 M of dust.this is important because it is thought that CCSNe could be responsible for some of the dust in the Universe, particularly the large amounts of dust observed in some early galaxies (De Looze et al., 2017). However, there are only three known SNRs with a significant amount of supernova ejecta dust: Cassiopeia A (Cas A), SN 1987A, and the Crab. The structure of a typical shell-type SNR is shown in Figure 3.4. Of the 294 known Galactic SNRs in the latest Green s Catalogue (Green, 2014) 234 are considered to be shell-type. The basic features of SNRs were discussed in Section 1.1. Filled-centre (FC) SNRs, or plerions, are SNRs similar to the Crab. FC SNRs have a flat, non-thermal radio spectral index and are centrally bright in X-rays. They lack a large-scale bright shell (the forward shock). It is usually assumed that these FC remnants are powered by a central pulsar (Wallace et al., 1997). Only

44 Chapter 3: Pulsar Wind Nebula G of the 294 known Galactic SNRs are classified as FC (Green, 2014). A schematic of an FC remnant with a PWN is shown in Figure 1.3. If an FC remnant has a shell it is called a composite SNR. Both Cas A and SN 1987A are shell-type SNRs. Cas A, shown in Figure 1.2, is 330 years old and recent modelling suggests the presence of M of SN ejecta dust. SN 1987A is, as the name suggests, 30 years old. Matsuura et al. (2015) suggest a dust mass of up to 0.8 M within SN 1987A. However, as young shell-type SNRs both Cas A and SN 1987A have forward and reverse shocks. Simulations find that between 1 and 80% of the dust is likely to be destroyed by the reverse shock. For both of these SNRs at least 25% of the dust must survive to fit the theoretical model of CCSNe producing M of dust. The Crab, however, does not have a forward or reverse shock. It is an FC SNR and contains between 0.11 and 0.48 M of dust (Owen & Barlow, 2015). Currently, PWN G is classified as an FC SNR (Green, 2014). Due to the similar morphology of their PWNe, PWN G is considered a close-cousin of the Crab Nebula. As we will discuss below, PWN G fits in with Cas A, SN 1987A and the Crab as a dusty SNR as it is surrounded by a bubble of cold SN ejecta dust. However, it is still an open question as to whether PWN G possesses an SNR shell or not The infrared dust bubble around PWN G An IR bubble of dust surrounding PWN G was discovered by Koo et al. (2008) using the Akari (ASTRO-F) satellite. A Spitzer observation of the IR bubble is shown in Figure 3.5. The X-ray emission fills the IR cavity, strongly suggesting an association. Until recently there were two theories regarding the origin of this loop: a star-forming region containing young stellar objects (YSOs) and SN ejecta dust heated by late O- and B-type stars IR-excess stellar objects The IR observations of PWN G reveal point-like sources in the IR observations. These point-like sources were first classified as YSOs by Koo et al. (2008) using Spitzer and Akari observations. Later, they were classified by Temim et al. (2010) as early-type stars. Kim et al. (2013) investigate the IR-excess objects further and find them to be late O- and early B-type main-sequence stars between O8 and B A star-forming region It was suggested by Koo et al. (2008) that the IR dust bubble is a star-forming region with embedded YSOs. They used Spitzer and Akari IR observations to identify at least 11 YSOs in the dust shell and conclude

45 Chapter 3: Pulsar Wind Nebula G Figure 3.5: A three-colour image of PWN G Chandra X-ray observations are in blue, Mercator Telescope (MAIA) 890 nm optical observations are shown in green and Spitzer (MIPSGAL) 24 µm IR observations are shown in red. We acquired the MAIA data while visiting the Mercator Observatory as part of the Observation Project course at the University of Amsterdam. that these objects are massive pre-main-sequence stars with T eff = K, M = M and L = (1 10) 10 4 L. Koo et al. (2008) identified the dust shell as the dust cloud that the massive progenitor of PWN G formed in. They suggest that when the massive progenitor of PWN G was in its post-mainsequence phase it produced a stellar wind bubble that compressed the surrounding dust. This compression then triggered the formation of the YSOs. Kim et al. (2013) find it more likely that the star formation was triggered during the main-sequence, late-main-sequence or post-main-sequence phase of the progenitor.

46 Chapter 3: Pulsar Wind Nebula G Supernova ejecta dust The first suggestion that the dust bubble was formed by the supernova explosion itself was by Temim et al. (2010). As the dust has a velocity greater than 500 km s 1 and the PWN fills the shell Temim et al. (2010) found it likely that the dust is SN ejecta dust. In the scenario that the bubble is entirely composed of SN ejecta dust, it is more likely that the point sources are early-type stars from the cluster in which the SN exploded. Temim et al. (2010) discount the idea of the shell being a pre-exiting IR shell as the shell has a high expansion velocity of 500 km s 1 as well as a lack of thermal X-ray emission. They also find it puzzling that there is no evidence of interaction between the SN explosion that formed PWN G and the IR bubble. If the SN explosion had encountered a pre-existing dust shell a reverse shock would be expected, which would disrupt the PWN. There is no evidence that this is the case. The latest investigation of the IR dust bubble, by Temim et al. (2017), places PWN G in the same category as the Crab Nebula, Cas A and SN 1987A; the only known SNRs with a significant amount of cold, SN ejecta dust. Temim et al. (2017) review prior investigations of the IR dust shell and investigate additional IR observations (shown in Table 3.3). They conclude that PWN G is similar to the Crab in that it is a PWN expanding into and radiatively heating a shell of cold SN ejecta dust. They derive that the dust has a lower mass limit of 0.3 M The forward shock Lu et al. (2002) find no evidence in their Chandra observations of PWN G of a thermal shell remnant (or forward shock) surrounding the PWN. Lang et al. (2010) are the first to suggest a large-scale shell surrounding PWN G They observe a possible 8 radius shell using 4.7 GHz and 1.4 GHz VLA observations, shown in Figure 3.6. However, the VLA at 1.4 GHz is not ideal for observing extended emission with an angular size greater than 5 and they do not have the data to measure a reliable spectral index. Bocchino et al. (2010) detect a faint diffuse X-ray shell surrounding PWN G with SUZAKU and XMM-Newton observations. The shell has a radius of 5.7 and an irregular shape. They conclude that this may be a thermal SNR shell, but require further observations to confirm whether the X-ray emission is thermal or non-thermal Detecting the forward shock at LOFAR frequencies Lang et al. (2010) find a maximum flux density of the shell candidate of µ Jy beam 1 at 1.4 GHz. More recently, Anderson et al. (2017) use THOR+VGPS observations to find an integrated flux

47 Chapter 3: Pulsar Wind Nebula G Figure 3.6: VLA 1.4 GHz observation of PWN G (Lang et al., 2010). The possible shell is circled in red and PWN G is the bright central source. density of 1.46 ± 0.28 Jy at 1.4 GHz for a shell of radius 7.2. Using this it is possible to calculate the approximate expected flux density of the shell at LOFAR HBA frequencies if it is in fact an SNR shell. If the shell detected by Lang et al. (2010) and Anderson et al. (2017) in Figure 3.6 is an SNR forward shock shell, then it would be expected to have a spectral index of 1 α 0 where S ν ν α for S ν the flux density in Jy and ν the frequency in Hz (Onić, 2013). Typically, SNRs have a spectral index of α 0.5. In our HBA observations we can confidently detect objects with flux densities of 100 mjy beam 1. Anderson et al. (2017) measure an integrated flux density of 1.46 Jy at 1.4 GHz for the shell candidate around PWN G For spectral indices of α = 0 and α = 0.5 the shell would be expected to have an integrated flux density of 1.46 Jy and 4.46 Jy respectively at 150 MHz. Assuming the SNR fills a circular region of radius 7.2, these integrated flux densities convert to average flux density values of Jy beam 1 and Jy beam 1 respectively in our 150 MHz HBA observation. These flux

48 Chapter 3: Pulsar Wind Nebula G Figure 3.7: LOFAR HBA MFS image of PWN G and the surrounding environment. The white circles are known SNRs from Green s SNR catalogue (Green, 2014) and the dashed white circles are SNR candidates from Anderson et al. (2017). The red, green and yellow circles are known, group and radio quiet HII regions respectively from the WISE HII region catalogue (Anderson et al., 2014). The synthesised beam is shown in the bottom right corner. densities are detectable in our HBA observations, but there is no evidence of extended emission around PWN G , as can be seen in Figure 3.7, aside from the known HII region G (Anderson et al., 2014). 3.3 Spectral energy distribution The properties of a PWN, and the pulsar and SNR associated with a PWN, can be investigated by modelling the SED of the PWN with an evolution model. The model can then be used to find the mass of the progenitor star, the initial spin period of the pulsar, the initial properties of the SN and more (Gelfand et al., 2015). The SED of a PWN can also be used to investigate how particles are produced and accelerated in the neutron star magnetosphere and to investigate the lowest energy emitting electrons. The lowest energy emitting electrons are observed in the radio and can act as a calorimeter for the total energy injected into the PWN (Gelfand et al., 2015). The latest PWN evolution modelling of PWN G was performed by Gelfand et al. (2015). The observations and the evolution model fit to the observations are shown in Figure 3.8. The evolution model

49 Chapter 3: Pulsar Wind Nebula G Figure 3.8: SED of PWN G from Gelfand et al. (2015). The orange points are VLA fluxes. The green line is the Chandra X-ray integrated flux. The blue points are VERITAS high-energy gamma-ray measurements. The black line is a fit based on their PWN evolution model Photon Energy [ev] νfν [erg s 1 cm 2 ] VERITAS XMM Chandra IR Radio VLA TGSS MSSS VLSSr Frequency ν [Hz] Figure 3.9: SED of PWN G using archival observations discussed in Section Note that the IR points measure the flux density of the SN ejecta dust around PWN G , not the PWN itself.

50 Chapter 3: Pulsar Wind Nebula G Photon Energy 10-6 [ev] 10-5 νfν [erg s 1 cm 2 ] VLA power law VLA+TGSS broken power law Archival radio power law Taylor et. al. (1996) Velusamy & Becker (1988) Velusamy & Becker (1988) Condon et. al. (1989) Caswell & Haynes (1987) Velusamy & Becker (1988) Reich et. al. (1984) Reich et. al. (1985) Velusamy & Becker (1988) Altenhoff et. al. (1979) Griffith et. al. (1990) Handa et. al. (1987) Hurley-Walker et. al. (2009) VLA TGSS MSSS VLSSr HBA Frequency ν [Hz] Figure 3.10: Radio SED of PWN G using archival radio observations discussed in Section The power laws are a result of fitting a power law or broken power law to the data, they are not a result of the PWN evolution model by Gelfand et al. (2015). The VLA power law is a simple power law fit to only the VLA observations. It has a spectral index of α = The VLA+TGSS broken power law is a broken power law fit to the VLA and TGSS points. It has spectral indices of α 1 = 0.03 and α 2 = The archival radio power law is a simple power law fit to all the radio points (excluding the LOFAR points). It has a spectral index of α = 0.13, the same spectral index found by Velusamy & Becker (1988) (see Sec ). fits a broken power law to the radio part of the spectrum. Numerical simulations predict a relativistic Maxwellian with a high-energy power law tail instead. As such, it is important to further investigate the radio spectrum of PWN G LOFAR flux density measurements would help to differentiate the different types of radio spectrum models, due to the low-frequency of the LOFAR observations. During our investigation of PWN G we found many archival flux density measurements that were not used by Gelfand et al. (2015) to fit the evolution model. We have already discussed these observations in Section We have plotted the full SED of PWN G in Figure 3.9 not including the LOFAR flux densities. Even without the LOFAR points there is much more information available on PWN

51 Chapter 3: Pulsar Wind Nebula G Flux density (Jy) Gal. synch. background Briggs 1.0 HBA Briggs 0.6 HBA combined HBA VLSSR TGSS WSRT Frequency (MHz) Figure 3.11: Low-frequency radio SED of PWN G showing the TGSS ADR, VLSSR, and WSRT points and the LOFAR HBA points using Briggs 1.0, Briggs 0.6, and Briggs 1.0 averaged/combined images. The background brightness temperature is also plotted. In the two lowest frequency HBA SBs confusion with the HII region surrounding PWN G results in higher flux density measurements. G In particular, as shown in Figure 3.10, the flux densities measured at less than 400 MHz support a simple or broken power law instead of a relativistic Maxwellian. PWN G is not detected in the VLSSR point source survey. The VLSSR postage-stamp image of PWN G has an RMS noise of Jy beam 1 and a synthesised beam size of 75. As such, in Figures 3.9 and 3.10 the VLSSR measurement is a 5σ upper limit based on the RMS noise in the VLSSR image. The LOFAR HBA flux density measurements are significantly higher than expected and appear to peak in the centre of the LOFAR band. Initially, this was thought to be the intrinsic in-band spectrum of LOFAR. However, as demonstrated in Section 2.4, the other point sources in the FoV do not show the same frequency structure. Therefore, this bump is unlikely to be caused by the sensitivity of LOFAR. An alternative issue could be confusion with the surrounding HII region. Using Briggs 1.0 weighting results in a synthesised beam that is larger than PWN G Therefore, we measured the flux density of PWN G using a Briggs 0.6 weighting clean. Using this clean the synthesised beam is approximately the size of PWN G As can be seen in Figure 3.11, changing the Briggs weighting did not significantly affect the flux density measurements. We also averaged together the Briggs 1.0 FITS files, but again this does not significantly affect the SED. Further investigation, preferably a re-observation of the FoV (in the hope of much more tractable ionospheric conditions), would be required to determine whether this bump feature is real or is an instrumental or calibration effect, and to correct for the steepening of the point source spectra. As discussed in Section 2.4 a possible method for improving the flux density measurements could be to apply a stricter uv-cut to resolve out extended emission that may be being added to the known sources in the FoV. This is future work for this project, and it could explain the unexpectedly high flux

52 Chapter 3: Pulsar Wind Nebula G density measured for PWN G If the bump in the spectrum of PWN G is real, this could indicate the presence of a relativistic Maxwellian or it could indicate the low-energy cut-off. These possibilities currently very tentative.

53 Chapter 4 The G Field This chapter was written in collaboration with Vladimír Domček (X-ray analysis) and Jacco Vink (interpretation). The Galactic Plane is interesting to observe across the electromagnetic spectrum including low radio frequencies, which to date have arguably been underexplored. HII regions, stellar wind bubbles, PWNe, SNRs, the ISM and more can be investigated. As PWN G lies in the Galactic Plane its location is covered by various Galactic Plane surveys at different frequencies. According to the latest catalogue by Green (2014) there are 294 known Galactic SNRs. About 90% of these SNRs have been found in radio surveys (Anderson et al., 2017). However, statistical calculations suggest that observers are missing more than 700 SNRs in the Milky Way (e.g. Li et al. 1991). Obtaining a more complete record of the SNR population is important as it leads to better estimates of the Galactic supernova rate, the maximum ages of SNRs, and because SNRs are obvious locations to search for young pulsars. The incompleteness of the SNR catalogue could be due to several observational biases, including inconsistent observational coverage of the Galactic Plane (particularly avoiding regions near bright radio sources) and different sensitivities for the surveys (which also depends on the complexity of the observed fields). In particular, low radio surface brightness SNRs may have been missed and compact SNRs (Green, 2005) may have been mistaken for HII regions. Confusion with HII regions is less of a problem at low frequencies ( 350 MHz) due to the typically steeper spectral indices of SNRs (α 0.5; Onić, 2013) as compared to HII regions (α 0). This means that SNRs are brighter at lower frequencies, while HII regions are brighter at higher frequencies. Therefore, wide-bandwidth radio observations are useful for detecting and differentiating between SNRs and HII regions. However, there have been relatively few low-frequency surveys of the Galactic Plane. A good illustration of the capability of such surveys for SNR searches was demonstrated by a 333 MHz survey with the Very Large Array (VLA) of the Galactic Centre region (Brogan et al., 2006). 36

54 Chapter 4: The G Galactic Plane Field 37 This survey resulted in the discovery of 35 new candidate SNRs. Since then several new SNRs have been discovered, the most recent discoveries being G in the radio by Kothes et al. (2017) and two HESS SNRs by Gottschall et al. (2016). While this analysis was being performed Anderson et al. (2017) identified 76 new SNR candidates found using THOR+VGPS observations. Here we will discuss the discovery of a new Galactic Plane SNR, G , as well as discuss other SNRs and SNR candidates in the same LOFAR FoV. 4.1 Observations Radio observations The FoV of the Galactic Plane around PWN G has been imaged using the VLA as part of the VLA Galactic Plane Survey (VGPS) with a beam size of 1 1 (Stil et al., 2006) 1. It has also been observed using WSRT as part of a Galactic Plane point source survey at 327 MHz with a beam size of by Taylor et al. (1996) 2. Both the VLA and WSRT mosaics are shown in Figure 4.1. In Figure 4.2, known SNRs, candidate SNRs and HII regions are shown in a 3-colour image combining the VLA, WSRT and LOFAR observations Infrared observations The Galactic Plane, including the FoV around PWN G , was observed at 24 µm as part of the Multiband Infrared Photometer for Spitzer GALactic Plane (MIPSGAL) survey (Carey et al., 2009; Gutermuth & Heyer, 2015), shown in Figure 4.1. MIPSGAL 24.0 µm observations have a resolution of 6 and a 5σ root-mean-squared (RMS) sensitivity of 1.3 mjy. The FoV was also observed at a much lower resolution at 12, 25, 60 and 100µm by the InfraRed Astronomy Satellite (IRAS; Neugebauer et al., 1984). 4.2 Supernova Remnants and candidates G We first identified G as a potential SNR in LOFAR HBA observations using the Core and Remote stations (van Haarlem et al., 2013). We can see in Figure 4.2 that both G and known SNR HC 40 (Green, 2014) stand out as bright objects at LOFAR frequencies. Figure 4.2 was produced by performing a multi-frequency (MFS) weighting clean on all measurement sets

55 Chapter 4: The G Galactic Plane Field 38 Figure 4.1: Observations of the Galactic Plane FoV where the LOFAR HBA observation, VGPS mosaic and WSRT mosaic coincide. Top: VGPS 1.4 GHz mosaic. Centre: WSRT 327 MHz mosaic. Bottom: Spitzer 24.0 µm MIPSGAL mosaic.

56 Chapter 4: The G Galactic Plane Field 39 Figure 4.2: Observations of the Galactic Plane at 1.4 GHz (blue, VLA), 327 MHz (green, WSRT) and 144 MHz (red, LOFAR). The synthesized beam sizes are shown in the bottom left corner. Known SNRs from Green s SNR catalogue (Green, 2014) are circled in solid white and candidate SNRs from Anderson et al. (2017) are circled in dashed white. The red, green, yellow, and cyan circles are known, group, radio quiet and candidate HII regions respectively (Anderson et al., 2014). Figure 4.3: LOFAR HBA observations of G , PWN G , and SNR HC 40. Both panels have a frequency bandwidth of 1.95 MHz. The left panel has a central frequency of MHz and the right panel has a central frequency of MHz. The synthesised beam sizes are shown in the bottom right corner of both panels Radio observations A comparison of G at two HBA frequencies is shown in Figure 4.3. As LOFAR observes a wide range of frequencies it is clear in Figure 4.3 that G is brighter in the lower frequency image than the higher frequency image. The spectral shape of HC 40 can also been seen in Figure 4.3 and is extremely similar to that of G This implies that G , like HC 40 (α = 0.49, Caswell, 1985), has a spectral index α < 0. Given the spectral similarity of G and HC 40, we infer that the spectral index of G is close to α 0.5.

57 Chapter 4: The G Galactic Plane Field 40 Pointing RA Dec Duration (s) Expected sensitivity (µjy) A 19h29m57.3s 18d09m54.9s B 19h30m11.6s 18d09m54.9s C 19h29m43.0s 18d09m54.9s Table 4.1: Details of the pointings for the Arecibo pulsar search of G The expected sensitivity is quoted for a minimum signal-to-noise ratio of 15 and pulse duty cycle of 10%. The LBA observations of the FoV (see Sec. 2) were calibrated using the LOFAR standard imaging pipeline. While this calibration is very preliminary, both HC 40 and G are detected as bright extended emission, unlike the known HII regions in the FoV which are not visible in the LBA observations. The VGPS observations have the highest angular resolution of the radio observations of this FoV, as can be seen in Figure 4.4a. In these observations, and the HBA observations in Figure 4.3, G has a shell- or bubble-like morphology. The 1.4 GHz integrated flux density reported by Anderson et al. (2017) is 1.21 Jy. Using the WSRT observation we find an approximate integrated flux density of 2.5 Jy at 327 MHz. Using these values we estimate a spectral index of α Pulsar search If G contains a pulsar, then studying its properties could provide useful information about the age of and distance to the SNR. As such we wrote a proposal for Arecibo observations of G to search for a pulsar. The proposal was accepted (Project ID: P3201) and the full proposal can be found in Appendix C. The data were acquired by Jason Hessels in one observing session on 2017/06/21. We used the 7-beam Arecibo L-band Feed Array (ALFA) receiver in order to cover as much of the SNR as possible with a grid of three interleaved ALFA pointings. The SNR has a radius of approximately 5 and the ALFA receiver can cover a region of this size. We used the Mock spectrometers in the same observational setup as the Arecibo Pulsar Survey Using ALFA (PALFA) by Lazarus et al. (2015). We observed in two MHz-wide frequency SBs with central frequencies of and MHz with µm time sampling. Only the total intensity (Stokes I) was recorded. A test pulsar, PSR J , was observed for 200 s prior to the target observations. PSR J has a right ascension and declination of 19h28m42.6s and +17d46m29.6s, respectively, a period of 68.7 ms, and a DM of 177 pc cm 3. We observed the test pulsar to ensure that the Arecibo receivers were working as expected during the target observations. The MHz sub-band standard Presto plot of the test pulsar is shown in Figure 4.5, and confirms that the system was operating as expected. The target observations are described in Table 4.1. Three pointings were used to cover the SNR as there are gaps between the circular beams of the ALFA receiver. The full data analysis has not yet been performed.

58 Chapter 4: The G Galactic Plane Field 41 Figure 4.4: Observations of G at (a) 1.4 GHz using the VLA, (b) 24.0 µm using Spitzer and (c) X-rays using XMM-Newton. The dashed cyan contours are from LOFAR HBA MHz data (contour levels: 0, 0.25, 0.5, 0.75, 1.0 Jy beam 1 ) and the solid yellow contours are from VLA 1.4 GHz data (contour levels: 12, 14, 16, 18, 20, 22, 24 mjy beam 1 ) from the image in (a).

59 Chapter 4: The G Galactic Plane Field 42 Figure 4.5: Standard Presto diagnostic plot of the test pulsar PSR J at MHz. This shows the pulse profile as a function of time and frequency during the course of this observation Infrared observations The FoV around G was observed at 24.0 µm as part of the Multiband Infrared Photometer for Spitzer GALactic Plane (MIPSGAL) survey (Carey et al., 2009; Gutermuth & Heyer, 2015). MIPSGAL 24.0 µm observations have a resolution of 6 and a 5σ root-mean-squared sensitivity of 1.3 mjy. There is no object visible at 24.0 µm at the position of G The lack of infrared (IR) emission was noted by Anderson et al. (2017) and was their main reason for identifying G as a potential SNR. The MIPSGAL observation of G is shown in Figure 4.4b X-ray observations The position of G is partially covered by an XMM-Newton observation taken on 2008 Mar 29. (ObsID: ), confirming the existence of an extended X-ray source at the location of G

60 Chapter 4: The G Galactic Plane Field , particularly at the position of the radio-bright part of the putative shell 3. Although G lies at the edge of the detector in the XMM-Newton observation, the observation is important as it allows us to determine the nature of the X-ray emission through spectral analysis of the EPIC-MOS camera (Turner et al., 2001) data. We extracted the spectrum with the Science Analysis System (SAS) v14.0. Due to a failed CCD chip in MOS1 and the smaller FoV of the EPIC-PN detector only data from the MOS2 detector were used. The data were reduced using the emproc task and filtered using the event file for the background flaring. This resulted in 40.7 ks of cleaned exposure time. The source extraction region was a 1.8 radius circle centered on the extended X-ray source. The background was extracted using a region of the same size positioned in a nearby area of the detector devoid of X-ray sources. The resulting spectrum (Fig. 4.6) shows bright K-shell emission lines from magnesium, silicon, and sulfur and potential contributions from neon and iron around 1 kev. This is typical for thermal emission from an optically thin plasma. To perform the spectral analysis the SPEX fitting package version 3.03 was used (Kaastra et al., 1996). The fitting statistics method employed was C-statistics (Cash, 1979). Abundances were expressed with respect to Solar Abundance values of Lodders et al. (2009). For the emission measure parameter (n e n H V ) we assumed a distance of 6 kpc. The analysis of the spectra was performed in the energy range between kev, as this is the range in which the source spectrum dominates the background. Additionally the range kev was excluded due to the presence of an Al Kα instrumental background line. After the background was subtracted the source spectrum consisted of 2000 counts. The spectrum was fitted with a non-equilibrium ionization (NEI) model with Galactic absorption. The Galactic background was represented by the model hot in SPEX, with the temperature fixed to 0.5 ev to mimic a neutral gas (de Plaa et al., 2016). The NEI model was employed with the following free parameters: electron temperature T 2, ionization age τ = n e t, normalization n e n H V, and abundances of elements Ne, Mg, Si, S, Fe. These elements have line emission in the energy band from keV, the band for which there was sufficient signal to noise. The best-fit model is represented by a C-stat / d.o.f. of 50.99/30. The parameters and 1σ errors are listed in Table 4.2 while the best fit model is shown in Figure 4.6. The ionization age informs us how far out of ionization equilibrium the plasma is. But given the narrow spectral range, the parameter may correlate with the best fit electron temperature T 2. To test the robustness of our best fit ionization age we calculate the error ellipse of τ and T 2, as shown in Figure 4.6. These results indicate that the plasma of the source has relatively high temperature T kev. The ionization age τ s cm 3 is much lower than needed for ionization/recombination balance (τ s cm 3 ). The fact that the spectrum is clearly out of ionization equilibrium is a clear signature that 3 The X-ray source is also detected at the edge of the FoV of two ROSAT PSPC observations (ObsIds: WG500042P.N2 and WG500209P.N1). However, since the spectral resolution of the ROSAT PSPC is poor and the image noisy, we do not present those data here.

61 Chapter 4: The G Galactic Plane Field 44 Parameter Unit Value Element Abundance N H cm Ne n e n H V cm Mg T 2 kev Si τ s cm S Fe Cstat/d.o.f /30 Table 4.2: The XMM-Newton best-fit model results. The abundances are provided in Solar units. Figure 4.6: Left: X-ray spectrum showing the best-fit model. The Al Kα instrumental background line around 1.49 kev has been blanked out. Right: Contour plots of the ionization age and post-shock temperature. the source is an SNR (Vink, 2012b), as no other other known source class has gas tenuous enough and/or young enough to be far out of ionization equilibrium High energy observations The High Energy Stereoscopic System CATalogue (HESSCAT) and Third Fermi-LAT Catalogue of High- Energy Sources (3FGL) were searched. There are no known nearby HESS or Fermi sources that are not identified as associated with other objects, such as PWN G Discussion G is not identified in Green s SNR catalog (Green, 2014) nor is it a known HII region in the WISE catalog (Anderson et al., 2014). It was detected as a source, but not identified as an SNR, by Day et al. (1972) in a Galactic Plane survey at 2.7 GHz. G was identified as an SNR candidate by Anderson et al. (2017).

62 Chapter 4: The G Galactic Plane Field The distance to G Estimating the distances of Galactic SNRs is notoriously difficult. There are few methods that give reliable results, such as kinematic methods based on optical Doppler shifts combined with proper motion of optical filaments (e.g. Reed et al., 1995, for Cas A) or, less reliably, 21cm line absorption combined with a Galactic rotation model. In contrast, SNRs located in the Magellanic Clouds can be reliably placed at the distance of these satellite galaxies. By using reliable distance estimates some secondary distance indicators have been developed, such as the so-called Σ D relation (e.g Pavlovic et al., 2014) or using the X-ray Galactic absorption column density to estimate the distance (Strom, 1994a). Here we explore both these methods to provide a distance estimate to G A first indication of the distance of an SNR can be its positional association within a spiral arm. However, the reason that the investigated field is so rich in sources is that the line of sight crosses the Sagittarius- Carina arm tangentially and regions of the Perseus arm. Taking the Galactic spiral arm model of Hou et al. (2009), we find that the l = 53.4 line of sight intercepts the Sagittarius arm (arm -3 in Hou et al., 2009) between 4 kpc and 7.5 kpc, and the Perseus arm at 9.6 kpc. Given that the Sagittarius arm is tangential along the line of sight, suggests a probable distance between 4.5 and 7.5 kpc. Strom (1994b) derived a relation between column density and distance of N H = d 1.58 cm 2. The measured column density of N H = cm 2 (Table 4.2), therefore, suggests a distance of 8 kpc. However, one should be cautious here because the line of sight crosses the arm tangentially, which is likely to lead to a column density that is higher than average for a given distance. The surface brightness of G normalized to 1 GHz is Σ = W m 2 Hz 1 sr 1. The 1 GHz surface brightness was obtained using the 1.4 GHz flux density measured by Anderson et al. (2017) and a spectral index of α = 0.49 (see Sec ). Using the relation between diameter and surface brightness in Pavlovic et al. (2014) gives a distance estimate of 7.4 kpc. Although both the column density and the Σ D relation give only rough estimates, they both suggest a location on the far side of the Sagitarius-Carina arm. We therefore adopt a distance of 7.5 kpc for G The angular radius of 5 translates then into a physical radius of 10.9d 7.5 pc, with d 7.5 the distance in units of 7.5 kpc The age of G G is associated with an X-ray source that has the spectral characteristics of an SNR: it has bright emission lines and is best characterized by an NEI model. In the Galaxy, only SNRs are young and tenuous enough to have NEI spectra (Vink, 2012b). The spectrum allows us to put some constraints on the density and age of the SNR. For that we need a volume estimate. Given a typical volume filling fraction

63 Chapter 4: The G Galactic Plane Field 46 of 25% 4 and assuming a spherical morphology, we estimate the volume to be V SNR = d cm3. The X-ray spectrum was obtained for only 20% of the shell, so we take V X d cm3 to be the volume pertaining to the X-ray spectrum. Taking n e 1.2n H in the emission measure n e n H V, we obtain the density n H 0.6d 3/2 7.5 cm 3. Using this number together with the best fit ionization age of n e t = cm 3 s we find an approximate age of 2700d 3/2 7.5 yr. The measured electron temperature corresponds to a shock velocity of V s 800 km s 1 or higher if the electron temperature is lower than the ion temperature (Vink, 2012b). For the Sedov-Taylor self-simular evolution model we have V s = 0.4R/t. Using R = 10.9d 7.5 pc gives then an approximate age of 5300d 7.5 yr. Using the Sedov-Taylor evolution model of R 5 = 2.026Et 2 /ρ, with E = erg gives yet another estimate of the age of 7800d 7/4 7.5 yr, consistent with the other estimates, which also suggests that the shell must have been created with an energy typical of supernovae. Taking all of these characteristics together is strong evidence that G is indeed an SNR with an age between 2000 and 8000 yr. X-ray observations centered on and covering the whole SNR are needed to better characterize the properties of G We note here that the low abundance of Ne is surprising and needs to be verified. Ne has line emission around 1 kev, where the background is quite high, and where the emission is most strongly affected by Galactic absorption. Therefore, Ne is more sensitive to systematic errors caused by background subtraction Other supernova remnants and candidates in the field of view Six of the SNR candidates identified by Anderson et al. (2017) are in the LOFAR FoV discussed here, including G , and are shown in Figure 4.2. The five other candidates are G , G , G , G , and G In the HBA observations of the FoV we can confidently detect sources with flux densities of 100 mjy beam 1. The VGPS, WSRT, and LOFAR HBA observations of these SNR candidates are shown in Figures 4.7 and 4.8. There are no sensitive X-ray observations (such as with Chandra or XMM-Newton) of the six SNR candidates in the FoV, except for the XMM-Newton observation of G The shell around PWN G was discussed in Section There is no evidence of extended emission around PWN G , as can be seen in Figure 4.8 (bottom panel), aside from the known HII region G (Anderson et al., 2014). Performing a similar calculation for SNR candidate G as for PWN G (see Sec ) using the reported flux of 5.24 Jy at 1.4 GHz (Anderson et al., 2017) we would expect average flux densities of Jy beam 1 and Jy beam 1 for spectral indices of α = 0 and α = 0.5 respectively. These values assume the SNR fills a circular region of radius We do not detect G and it can be seen in Figure 4.7 (center panel) that G becomes fainter at lower frequencies. 4 A strong shock has a compression factor of 4. This means that roughly 25% of the volume, approximated by a sphere, will emit.

64 Chapter 4: The G Galactic Plane Field 47 Figure 4.7: SNR candidates from Anderson et al. (2017) in the FoV. In each row the left panel is the VGPS 1.4 GHz observation, the center panel is the WSRT GHz observation, and the right panel is the LOFAR GHz observation. From top to bottom the rows are the SNR candidates (circled in dashed white) from Anderson et al. (2017): G , G , and G The solid yellow circles are HII regions from the WISE HII catalog (Anderson et al., 2014). The synthesised beams are shown in the bottom left corner of each image.

65 Chapter 4: The G Galactic Plane Field 48 Figure 4.8: SNR candidates from Anderson et al. (2017) in the FoV. In each row the left panel is the VGPS 1.4 GHz observation, the center panel is the WSRT GHz observation, and the right panel is the LOFAR GHz observation. From top to bottom the rows are the SNR candidates (circled in dashed white) from Anderson et al. (2017): G and G The solid yellow circles are HII regions from the WISE HII catalogue (Anderson et al., 2014). The synthesised beams are shown in the bottom left corner of each image. From the flux density measured by Anderson et al. (2017), we do not expect to detect G unless it has a steep spectrum. For α = 0 we expect an average flux density of Jy beam 1 and for α = 0.5 we expect an average flux density of Jy beam 1. It is difficult to determine from Anderson et al. (2017) exactly what emission is G , as can be seen in Figure 4.8 (top panel). As such it is not clear whether G is detected in the LOFAR HBA observations. We easily detect G and it has a similar morphology to the VLA morphology reported in Anderson et al. (2017), as can be seen in Figure 4.7 (top panel). G is also bright in the LBA observations, similar to G and SNR HC 40. There is no XMM-Newton or Chandra observations in the direction of G , which would be needed to confirm it as an SNR, but we find it to be a good SNR candidate.

66 Chapter 4: The G Galactic Plane Field 49 Flux density (Jy) SNR 1.4 GHz GHz α G ± ± G ± ± G ± ± Table 4.3: Flux densities of SNR candidates at 1.4 GHz from Anderson et al. (2017) and 327 MHz measured using WSRT observations (Taylor et al., 1996). The WSRT errors are 3σ statistical errors based on the RMS noise in the image; these errors do not take other sources of error, such as confusion, into account. While we do detect known SNR G it has a very low surface brightness. This is more consistent with the low flux densities measured by Matthews et al. (1998) than the extremely high flux density measured by Anderson et al. (2017). The results for G , G , G , and G are supported by flux density measurements from the WSRT observations. This is shown in Table 4.3. We do not measure a flux density for G as it has a small angular size (a radius of only 1, Anderson et al., 2017). In Figure 4.7 we can see that there is some extended emission around G that may or may not be part of the candidate and would contribute to confusion in the WSRT and LOFAR HBA observations due to the large synthesised beam sizes. The discovery of a new SNR in the Galactic Plane, and the analysis of five more Galactic SNR candidates, shows the utility of LOFAR Galactic Plane observations. The Anderson et al. (2017) THOR+VGPS survey found 76 new Galactic SNR candidates. In just one FoV LOFAR helped to confirm one candidate, support one candidate, clearly discount two candidates, and tentatively discount another candidate. This shows that LOFAR is a very useful telescope for investigating SNRs and should be used to investigate other Northern Galactic SNR cadidates, and possibly find some more candidates of its own.

67 Chapter 5 Summary In this project we presented the challenging process of analysing LOFAR observations of the Galactic Plane. We presented four possible calibration methods: the MSSS imaging pipeline, Pre-Facet-Cal, direction independent zero-phase calibration, and direction independent non-zero-phase calibration. We showed that, due to the extended emission in the FoV and the prominent ionospheric effects in our observations, direction independent non-zero-phase calibration was the best method for our LOFAR HBA observations. We discussed imaging using WSClean, and point source detection and flux density measurements with pybdsf. The main object of interest for this project was PWN G We investigated archival observations and measurements of PWN G to improve the SED and investigate the lowest-frequency observations. The LOFAR HBA flux density measurements are not consistent with a power law or broken power law and, if real, could show a relativistic Maxwellian feature or the low-energy cut-off. We showed that the higher and steeper than expected measured flux densities are not due to confusion or synthesised beam size. Possible other causes may include ionospheric effects, instrumental or calibration effects, or increased flux densities due to the large scale emission in the FoV. More work is required to demonstrate that the LOFAR flux density measurements are real, such as re-observing the field and investigating uv-cuts to remove the effects of extended emission. We used the LOFAR HBA observations in combination with VLA and WSRT observations to show that, contrary to previous suggestions, PWN G does not have a forward shock shell. This shows that G is a filled-centre SNR, not a composite SNR. G is also one of only four known SNRs with a significant amount of cold supernova ejecta dust. We use the LOFAR HBA observations to show that SNR candidates G and G (Anderson et al., 2017) are unlikely to be SNRs as we do not detect them in the HBA observations. We show that G is a good SNR candidate requiring further investigation and that SNR G likely has a lower flux density than that reported by Anderson et al. (2017). 50

68 Chapter 5: Summary 51 Figure 5.1: Word Cloud summary of the most common words in this thesis. We independently discovered a new SNR, G , using our LOFAR observations. G has a shell-like morphology in the radio with a radius of 5. Using LOFAR LBA and HBA observations, as well as archival WSRT and VGPS observations, we confirm that G has a steep spectral index (α 0.5), typical of synchrotron radiation from SNRs. MIPSGAL observations show that G has no IR component. Archival XMM-Newton observations show that G has an associated X-ray component with a coincident morphology to the radio shell. Furthermore, analysis and fitting of the XMM-Newton observation show that G has strong emission lines and is best characterized by a non-equilibrium ionization model, with an ionization age and normalization typical for an SNR with an age between 2000 and 8000 yr and a density of n H 0.6d 3/2 7.5 cm 3. Given the X-ray, IR, and radio characteristics of G , we confirm that it is a new Galactic Plane SNR. We investigate five other SNR candidates from Anderson et al. (2017) in the LOFAR FoV. We show that three of these are unlikely to be SNRs while one, G , is a good SNR candidate. This demonstrates that LOFAR, with its large FoV, low observing frequencies, and wide-bandwidth, has the potential to be an excellent instrument for investigating SNR candidates with a Galactic Plane survey.

69

70 Appendix A Calibration pipeline functions Listing A.1: Functions required to run direction independent calibration. Particularly useful for calibrating Galactic Plane LOFAR observations. i m p o r t os d e f cal_flag_writer ( SB, calscan, cal_directory, working_directory, bl_cut=true ) : ' ' ' Write t h e p a r s e t to f l a g t h e c a l i b r a t o r scan Args : SB ( s t r ) : subband number c a l s c a n ( s t r ) : c a l i b r a t o r scan number ( eg. L ) c a l d i r e c t o r y ( s t r ) : path to f o l d e r c o n t a i n i n g c a l i b r a t o r measurement s e t s w o r k i n g d i r e c t o r y ( s t r ) : path to d i r e c t o r y to w r i t e f i n a l o u t p u t to kwargs b l c u t ( b o o l ) : i f True, c u t s a l l b a s e l i n e s l e s s than 100 lambda. I f F a l s e, no c u t D e f a u l t : True ' ' ' steps = ' [ f l a g e a r s, f l a g 1 3, a o f l a g g e r ] ' ears = ( ' [ [ CS001HBA0, CS001HBA1 ], [ CS002HBA0, CS002HBA1 ], ' ' [ CS003HBA0, CS003HBA1 ], [ CS004HBA0, CS004HBA1 ], ' ' [ CS005HBA0, CS005HBA1 ], [ CS006HBA0, CS006HBA1 ], ' ' [ CS007HBA0, CS007HBA1 ], [ CS011HBA0, CS011HBA1 ], ' ' [ CS013HBA0, CS013HBA1 ], [ CS017HBA0, CS017HBA1 ], ' ' [ CS021HBA0, CS021HBA1 ], [ CS024HBA0, CS024HBA1 ], ' ' [ CS026HBA0, CS026HBA1 ], [ CS028HBA0, CS028HBA1 ], ' ' [ CS030HBA0, CS030HBA1 ], [ CS031HBA0, CS031HBA1 ], ' ' [ CS032HBA0, CS032HBA1 ], [ CS101HBA0, CS101HBA1 ], ' ' [ CS103HBA0, CS103HBA1 ], [ CS201HBA0, CS201HBA1 ], ' ' [ CS301HBA0, CS301HBA1 ], [ CS302HBA0, CS302HBA1 ], ' 53

71 ' [ CS401HBA0, CS401HBA1 ], [ CS501HBA0, CS501HBA1 ] ] ' ) calms_orig = ' L345 '+ calscan + ' SB '+SB+ ' uv. dppp.ms ' calms_flagged = calms_orig + '. f l ' i f bl_cut : bl_line = e l s e : bl_line = ' msin. b a s e l i n e=cs & ' ' ' lines = [ ' msin= ' + cal_directory + calms_orig, ' msout= ' + working_directory + calms_flagged, ' msin. datacolumn=data ', bl_line, ' msin. a u t o w e i g h t=f ', ' msout. datacolumn=data ', ' s t e p s= ' + steps, ' f l a g e a r s. t y p e=p r e f l a g g e r ', ' f l a g e a r s. b a s e l i n e= ' + ears, ' f l a g 1 3. t y p e=p r e f l a g g e r ', ' f l a g 1 3. b a s e l i n e =[CS013 ] ', ' a o f l a g g e r. a u t o c o r r=f ', ' a o f l a g g e r. timewindow=0 ' ] fn = working_directory + ' calsb '+SB+ ' f l a g. p a r s e t ' thefile = open ( fn, 'w ' ) f o r line i n lines : thefile. write ( %s \n % line ) thefile. close ( ) p r i n t ' calsb '+SB+ ' f l a g. p a r s e t w r i t t e n ' r e t u r n fn d e f targ_flag_writer ( SB, targ_directory, working_directory, bl_cut=true ) : ' ' ' Write t h e p a r s e t to f l a g t h e t a r g e t scan Args : SB ( s t r ) : subband number t a r g d i r e c t o r y ( s t r ) : path to f o l d e r c o n t a i n i n g t a r g e t measurement s e t s w o r k i n g d i r e c t o r y ( s t r ) : path to d i r e c t o r y to w r i t e f i n a l o u t p u t to kwargs b l c u t ( b o o l ) : i f True, c u t s a l l b a s e l i n e s l e s s than 100 lambda. I f F a l s e, no c u t D e f a u l t : True ' ' ' steps = ' [ f l a g e a r s, f l a g 1 3, a o f l a g g e r ] ' ears = ( ' [ [ CS001HBA0, CS001HBA1 ], [ CS002HBA0, CS002HBA1 ], ' ' [ CS003HBA0, CS003HBA1 ], [ CS004HBA0, CS004HBA1 ], ' ' [ CS005HBA0, CS005HBA1 ], [ CS006HBA0, CS006HBA1 ], ' ' [ CS007HBA0, CS007HBA1 ], [ CS011HBA0, CS011HBA1 ], '

72 ' [ CS013HBA0, CS013HBA1 ], [ CS017HBA0, CS017HBA1 ], ' ' [ CS021HBA0, CS021HBA1 ], [ CS024HBA0, CS024HBA1 ], ' ' [ CS026HBA0, CS026HBA1 ], [ CS028HBA0, CS028HBA1 ], ' ' [ CS030HBA0, CS030HBA1 ], [ CS031HBA0, CS031HBA1 ], ' ' [ CS032HBA0, CS032HBA1 ], [ CS101HBA0, CS101HBA1 ], ' ' [ CS103HBA0, CS103HBA1 ], [ CS201HBA0, CS201HBA1 ], ' ' [ CS301HBA0, CS301HBA1 ], [ CS302HBA0, CS302HBA1 ], ' ' [ CS401HBA0, CS401HBA1 ], [ CS501HBA0, CS501HBA1 ] ] ' ) targms_orig = ' L SB '+SB+ ' uv. dppp.ms ' targms_flagged = targms_orig + '. f l ' i f bl_cut : bl_line = e l s e : bl_line = ' msin. b a s e l i n e=cs & ' ' ' lines = [ ' msin= ' + targ_directory + targms_orig, ' msout= ' + working_directory + targms_flagged, ' msin. datacolumn=data ', bl_line, ' msin. a u t o w e i g h t=f ', ' msout. datacolumn=data ', ' s t e p s= ' + steps, ' f l a g e a r s. t y p e=p r e f l a g g e r ', ' f l a g e a r s. b a s e l i n e= ' + ears, ' f l a g 1 3. t y p e=p r e f l a g g e r ', ' f l a g 1 3. b a s e l i n e =[CS013 ] ', ' a o f l a g g e r. a u t o c o r r=f ', ' a o f l a g g e r. timewindow=0 ' ] fn = working_directory + ' targsb '+SB+ ' f l a g. p a r s e t ' thefile = open ( fn, 'w ' ) f o r line i n lines : thefile. write ( %s \n % line ) thefile. close ( ) p r i n t ' targsb '+SB+ ' f l a g. p a r s e t w r i t t e n ' r e t u r n fn d e f cal_gaincal_writer ( SB, calscan, working_directory, sourcedb, sourcedb_path, bl_cut=true ) : ' ' ' Write t h e p a r s e t to f i n d t h e c a l i b r a t o r g a i n s o l u t i o n s Args : SB ( s t r ) : subband number c a l s c a n ( s t r ) : c a l i b r a t o r scan number ( eg. L ) w o r k i n g d i r e c t o r y ( s t r ) : path to d i r e c t o r y to w r i t e f i n a l o u t p u t to s o u r c e d b ( s t r ) : name o f t h e c a l i b r a t o r skymodel s o u r c e d b f i l e s o u r c e d b p a t h ( s t r ) : path to s o u r c e b d f i l e kwargs b l c u t ( b o o l ) : i f True, c u t s a l l b a s e l i n e s l e s s than 100 lambda. I f F a l s e, no c u t

73 D e f a u l t : True ' ' ' calms_orig = ' L345 '+ calscan + ' SB '+SB+ ' uv. dppp.ms ' calms_flagged = calms_orig + '. f l ' calms_gc = calms_flagged + '. gc ' skymodel = sourcedb_path + sourcedb parmdb = working_directory + calms_gc + ' / i n s t r u m e n t ' i f bl_cut : bl_line = e l s e : bl_line = ' msin. b a s e l i n e=cs & ' ' ' steps = ' [ g a i n c a l ] ' lines = [ ' msin= ' + working_directory + calms_flagged, ' msout= ' + working_directory + calms_gc, ' msin. datacolumn=data ', bl_line, ' msout. datacolumn=data ', ' s t e p s= ' + steps, ' g a i n c a l. s o u r c e d b= ' + skymodel, ' g a i n c a l. parmdb= ' + parmdb, ' g a i n c a l. c a l t y p e=d i a g o n a l ', ' g a i n c a l. usebeammodel=t r u e ', ' g a i n c a l. s o l i n t =2 ', ' g a i n c a l. m a x i t e r =200 ', ' g a i n c a l. u s e c h a n n e l f r e q=f a l s e ' ] fn = working_directory + ' calsb '+SB+ ' g a i n c a l. p a r s e t ' thefile = open ( fn, 'w ' ) f o r line i n lines : thefile. write ( %s \n % line ) thefile. close ( ) p r i n t ' calsb '+SB+ ' g a i n c a l. p a r s e t w r i t t e n ' r e t u r n fn d e f cal_applycal_writer ( SB, calscan, working_directory, zerophase=true, bl_cut=true ) : ' ' ' Write t h e p a r s e t to a p p l y t h e c a l i b r a t i o n g a i n s o l u t i o n s Args : SB ( s t r ) : subband number c a l s c a n ( s t r ) : c a l i b r a t o r scan number ( eg. L ) w o r k i n g d i r e c t o r y ( s t r ) : path to d i r e c t o r y to w r i t e f i n a l o u t p u t to kwargs z e r o p h a s e ( b o o l ) : i f True a p p l y t h e zero phase True s o l u t i o n s D e f a u l t=true b l c u t ( b o o l ) : i f True, c u t s a l l b a s e l i n e s l e s s than 100 lambda. I f F a l s e, no c u t D e f a u l t : True ' ' '

74 calms_orig = ' L345 '+ calscan + ' SB '+SB+ ' uv. dppp.ms ' calms_flagged = calms_orig + '. f l ' calms_gc = calms_flagged + '. gc ' calms_ac = calms_gc + '. ac ' i f zerophase : parmdb = working_directory + calms_gc + e l s e : parmdb = working_directory + calms_gc + ' / i n s t r u m e n t t i n d ' ' / i n s t r u m e n t ' i f bl_cut : bl_line = e l s e : bl_line = ' msin. b a s e l i n e=cs & ' ' ' steps = ' [ a p p l y c a l, applybeam, a o f l a g g e r ] ' lines = [ ' msin= ' + working_directory + calms_gc, ' msin. datacolumn=data ', bl_line, ' msout= ' + working_directory + calms_ac, ' s t e p s= ' + steps, ' a p p l y c a l. t y p e=a p p l y c a l ', ' a p p l y c a l. c o r r e c t i o n=g a i n ', ' a p p l y c a l. parmdb= ' + parmdb, ' applybeam. u s e c h a n n e l f r e q=f a l s e ' ] fn = working_directory + ' calsb '+SB+ ' a p p l y c a l. p a r s e t ' thefile = open ( fn, 'w ' ) f o r line i n lines : thefile. write ( %s \n % line ) thefile. close ( ) p r i n t ' calsb '+SB+ ' a p p l y c a l. p a r s e t w r i t t e n ' r e t u r n fn d e f targ_applycal_writer ( SB, calscan, working_directory, tind=true, bl_cut=true ) : ' ' ' Write t h e p a r s e t to a p p l y t h e c a l i b r a t i o n g a i n s o l u t i o n s to t h e t a r g e t Args : SB ( s t r ) : subband number c a l s c a n ( s t r ) : c a l i b r a t o r scan number ( eg. L ) w o r k i n g d i r e c t o r y ( s t r ) : path to d i r e c t o r y to w r i t e f i n a l o u t p u t to kwargs z e r o p h a s e ( b o o l ) : i f True a p p l y t h e zero phase True s o l u t i o n s D e f a u l t=true b l c u t ( b o o l ) : i f True, c u t s a l l b a s e l i n e s l e s s than 100 lambda. I f F a l s e, no c u t D e f a u l t : True

75 ' ' ' targms_orig = ' L SB '+SB+ ' uv. dppp.ms ' targms_flagged = targms_orig + '. f l ' targms_ac = targms_flagged + '. ac ' calms_orig = ' L345 '+ calscan + ' SB '+SB+ ' uv. dppp.ms ' calms_flagged = calms_orig + '. f l ' calms_gc = calms_flagged + '. gc ' i f tind : parmdb = working_directory + calms_gc + e l s e : parmdb = working_directory + calms_gc + ' / i n s t r u m e n t t i n d ' ' / i n s t r u m e n t ' i f bl_cut : bl_line = e l s e : bl_line = ' msin. b a s e l i n e=cs & ' ' ' steps = ' [ a p p l y c a l, applybeam, a o f l a g g e r ] ' lines = [ ' msin= ' + working_directory + targms_flagged, ' msin. datacolumn=data ', bl_line, ' msout= ' + working_directory + targms_ac, ' s t e p s= ' + steps, ' a p p l y c a l. t y p e=a p p l y c a l ', ' a p p l y c a l. c o r r e c t i o n=g a i n ', ' a p p l y c a l. parmdb= ' + parmdb, ' applybeam. u s e c h a n n e l f r e q=f a l s e ' ] fn = working_directory + ' targsb '+SB+ ' a p p l y c a l. p a r s e t ' thefile = open ( fn, 'w ' ) f o r line i n lines : thefile. write ( %s \n % line ) thefile. close ( ) p r i n t ' targsb '+SB+ ' a p p l y c a l. p a r s e t w r i t t e n ' r e t u r n fn d e f standard_wsclean ( scan, SB, suffix, infile_path, outfile_path, column= 'DATA ', clean_name= 'DATA ', bl_cut=true ) : ' ' ' Perform a b a s i c c l e a n Args : scan ( s t r ) : scan i n f o r m a t i o n s u f f i x ( s t r ) : f i l e e x t e n s i o n o f MS ( uv. dppp.ms + s u f f i x ) i n f i l e p a t h ( s t r ) : path to i n p u t f i l e o u t f i l e p a t h ( s t r ) : path to w r i t e o u t p u t to kwargs column ( s t r ) : e i t h e r DATA o r CORRECTED DATA. Column o f measurement s e t to c l e a n D e f a u l t : 'DATA ' clean name ( s t r ) : an e x t r a s t r to a t t a c h to t h e end o f t h e F I T S f i l e names i f d e s i r e d

76 D e f a u l t : 'DATA ' b l c u t ( b o o l ) : i f True, c u t s a l l b a s e l i n e s l e s s than 100 lambda. I f F a l s e, no c u t D e f a u l t : True ' ' ' infile = ' L345 '+ scan + ' SB '+SB+ ' uv. dppp.ms ' + suffix outfile = infile + ' '+clean_name clean = ( ' w s c l e a n s i z e s c a l e 30 a r c s e c m u l t i s c a l e minuv l ' ' 100 n i t e r 5000 datacolumn '+column+ ' name ' ) p r i n t ' C l e a n i n g '+ infile + ' ' os. system ( clean + outfile_path+outfile + ' ' + infile_path+infile ) d e f run_pipeline ( SB, calscan, targscan, zerophase=true, bl_cut=true ) : ' ' ' Run t h e f u l l c a l i b r a t i o n p i p e l i n e Args : SB ( i n t ) : SB number c a l s c a n ( s t r ) : c a l i b r a t o r scan nnumber ( eg. L ) kwargs z e r o p h a s e ( b o o l ) : i f True a p p l y t h e zero phase True s o l u t i o n s D e f a u l t=true b l c u t ( b o o l ) : i f True, c u t s a l l b a s e l i n e s l e s s than 100 lambda. I f F a l s e, no c u t D e f a u l t : True ' ' ' i f SB < 1 0 : SB = ' 00 ' + s t r ( SB ) e l i f SB < : SB = ' 0 ' +s t r ( SB ) e l s e : SB = s t r ( SB ) i f zerophase : SB_dir = 'SB '+SB+ ' t i m e i n d e p e n d e n t ' e l s e : SB_dir = 'SB '+SB+ ' t i m e d e p e n d e n t ' i f bl_cut : SB_dir = SB_dir + ' c o r e o n l y ' cal_directory = ( ' / data1 / d r i e s s e n / H B A c a l i b r a t o r / '+ calscan + ' scan / ' ) targ_directory = ' / data1 / d r i e s s e n /HBA/ ' working_directory = ( ' / data1 / d r i e s s e n / m y p i p e l i n e / ' ' w o r k i n g d i r / '+SB_dir+ ' / ' ) os. system ( ' mkdir '+working_directory ) sourcedb = ' 3C380 TGSS5Jy. s o u r c e d b ' sourcedb_path = ' / data1 / d r i e s s e n / t e s t p i p e l i n e / ' p r i n t ' W r i t i n g p a r s e t s ' p r i n t ' \n '

77 cal_flag_fn = cal_flag_writer ( SB, calscan, cal_directory, working_directory, bl_cut=bl_cut ) p r i n t ' \n ' targ_flag_fn = targ_flag_writer ( SB, targ_directory, working_directory, bl_cut=bl_cut ) p r i n t ' \n ' cal_gaincal_fn = cal_gaincal_writer ( SB, calscan, working_directory, sourcedb, sourcedb_path, bl_cut=bl_cut ) p r i n t ' \n ' cal_applycal_fn = cal_applycal_writer ( SB, calscan, working_directory, tind=time_independent, bl_cut=bl_cut ) p r i n t ' \n ' targ_applycal_fn = targ_applycal_writer ( SB, calscan, working_directory, tind=time_independent, bl_cut=bl_cut ) p r i n t ' \n ' p r i n t ' F l a g g i n g c a l MS: SB '+SB+ ' ' os. system ( 'NDPPP '+cal_flag_fn+ >' +working_directory+ calsb +SB+ f l a g. l o g ' ) p r i n t ' \n ' p r i n t ' F l a g g i n g t a r g MS: SB '+SB+ ' ' os. system ( 'NDPPP '+targ_flag_fn+ >' +working_directory+ targsb +SB+ f l a g. l o g ' ) p r i n t ' \n ' p r i n t ' Running g a i n c a l on c a l MS: SB '+SB+ ' ' os. system ( 'NDPPP '+cal_gaincal_fn+ >' +working_directory+ calsb +SB+ g a i n c a l. l o g ' ) p r i n t ' \n ' p r i n t ' Write time i n d e p e n d e n t c a l i n c a l MS: SB '+SB+ ' ' parmdb = ( working_directory + ' L345 '+ calscan + ' SB '+SB+ ' uv. dppp.ms. f l. gc / i n s t r u m e n t ' ) parmdb_tind = ( working_directory + ' L345 '+ calscan + ' SB '+SB+ ' uv. dppp.ms. f l. gc / i n s t r u m e n t t i n d ' ) os. system ( ' p a r m e x p o r t c a l i n= '+parmdb+ ' out= '+parmdb_tind+ ' z e r o p h a s e=t ' ) p r i n t ' \n ' p r i n t ' Apply time i n d e p e n d e n t c a l to CAL MS: SB '+SB+ ' ' os. system ( 'NDPPP '+cal_applycal_fn+ >' +working_directory+ calsb +SB+ a p p l y c a l. l o g ' ) p r i n t ' \n ' p r i n t ' Apply time i n d e p e n d e d c a l to TARG MS: SB '+SB+ ' ' os. system ( 'NDPPP '+targ_applycal_fn+ >' +working_directory+ targsb +SB+ a p p l y c a l. l o g ' ) p r i n t ' \n ' p r i n t ' C l e a n i n g t h e MS f i l e s ' standard_wsclean ( calscan, SB, '. f l ', working_directory, working_directory ) standard_wsclean ( calscan, SB, '. f l. gc ', working_directory, working_directory ) standard_wsclean ( calscan, SB, '. f l. gc. ac ', working_directory, working_directory ) standard_wsclean ( targscan, SB, '. f l ', working_directory, working_directory ) standard_wsclean ( targscan, SB, '. f l. ac ', working_directory, working_directory ) standard_wsclean ( targscan, SB, ' ', ' / data1 / d r i e s s e n /HBA/ ', working_directory ) standard_wsclean ( calscan, SB, ' ', ( ' / data1 / d r i e s s e n / H B A c a l i b r a t o r / ' +calscan+ ' scan / ' ), working_directory ) standard_wsclean ( targscan, SB, ' ', ' / data1 / d r i e s s e n /HBA/ ', working_directory,

78 column= 'CORRECTED DATA ', clean_name= 'CDATA ' ) standard_wsclean ( calscan, SB, ' ', ' / data1 / d r i e s s e n / H B A c a l i b r a t o r / '+calscan+ ' scan / ', working_directory, column= 'CORRECTED DATA ', clean_name= 'CDATA ' ) p r i n t ' \n ' p r i n t ' PIPELINE COMPLETE ' d e f combine_sbs ( min_sb, max_sb, scan, input_ext, input_path, output_ext, output_path ) : ' ' ' Write a p a r s e t to combine SBs i n t o one measurement s e t Args : min SB ( i n t ) : combine SBs from in SB to max SB max SB ( i n t ) : combine SBs from in SB to max SB scan ( s t r ) : scan number ( eg. 926 f o r L ) i n p u t e x t ( s t r ) : e x t e n s i o n o f i n p u t MSs ( eg. uv. dppp.ms + i n p u t e x t ) i n p u t p a t h ( s t r ) : path to i n p u t f o l d e r o u t p u t e x t ( s t r ) : e x t e n s i o n f o r o u t p u t MS o u t p u t p a t h ( s t r ) : path to o u tput f o l d e r ' ' ' files = ' [ ' f o r i i n r a n g e ( max_sb min_sb ) : SB = min_sb + i i f SB < 1 0 : SB = ' 00 ' + s t r ( SB ) e l i f SB < : SB = ' 0 ' +s t r ( SB ) e l s e : SB = s t r ( SB ) input_folder = ' t a r g p r o d u c t s / ' input_p = input_path + input_folder inp = input_p+ ' L345 '+ scan + ' SB '+SB+ ' uv. dppp.ms ' + input_ext + files += inp files_in = files [ : 1 ] + ' ] ' ', ' lines = [ ' msin= '+files_in, ' msout= '+output_path+ ' L345 '+scan+ ' SB '+s t r ( min_sb )+ ' SB '+s t r ( max_sb )+output_ext, ' s t e p s =[] ' ] fn = ' combinesb '+s t r ( SB )+ '. p a r s e t ' thefile = open ( fn, 'w ' ) f o r line i n lines : thefile. write ( %s \n % line ) thefile. close ( ) p r i n t ' W r i t t e n Combining SB '+s t r ( SB )+ ' p a r s e t '

79 Appendix B Measuring flux densities with pybdsf B.1 Script to convert VLSSR and WSRT catalogues to a usable format Listing B.1: Script to read the WSRT and VLSSR catalogue files and convert the data to units and formats consistent with the TGSS ADR and pybdsf catalogues. This script then writes new CSV files for the VLSSR and WSRT catalogues. params = { f i g u r e. f i g s i z e : ( 1 2, 9 ), f o n t. s i z e : 20, f o n t. w e i g h t : normal, x t i c k. major. s i z e : 8, x t i c k. minor. s i z e : 4, y t i c k. major. s i z e : 8, y t i c k. minor. s i z e : 4, x t i c k. major. width : 3, x t i c k. minor. width : 3, y t i c k. major. width : 3, y t i c k. minor. width : 3, x t i c k. major. pad : 8, x t i c k. minor. pad : 8, y t i c k. major. pad : 8, y t i c k. minor. pad : 8, l i n e s. l i n e w i d t h : 3, l i n e s. m a r k e r s i z e : 10, a x e s. l i n e w i d t h : 3, l e g e n d. l o c : b e s t, t e x t. u s e t e x : False, x t i c k. l a b e l s i z e : 20, y t i c k. l a b e l s i z e : 20, } i m p o r t matplotlib matplotlib. rcparams. update ( params ) i m p o r t os i m p o r t numpy as np 62

80 i m p o r t matplotlib. pyplot as plt from astropy i m p o r t units as u from astropy. coordinates i m p o r t SkyCoord from astropy. coordinates i m p o r t ICRS, Galactic, FK4, FK5 i m p o r t csv i m p o r t glob i m p o r t aplpy as apl i m p o r t matplotlib. cm as cm i m p o r t time i f name i n ' m a i n ' : # The l o c a t i o n s and names o f t h e # s o u r c e t a b l e f i l e s VLSSr_file = ( ' /home/ l a u r a / Documents / ' ' M s c P r o j e c t / V L S S r o r i g i n a l c u t. c s v ' ) WSRT_file = ( ' /home/ l a u r a / Documents / ' ' M s c P r o j e c t / WSRT 327sources. t s v ' ) # Import t h e VLSSr data u s i n g # s p e c i f i c column s i z e s VLSSr_raw = np. genfromtxt ( VLSSr_file, comments= '#', delimiter =(3, 3, 6, 4, 3, 5, 8, 5, 6, 6, 9, 9, 8, 7), usecols =(0, 1, 2, 3, 4, 5, 7) ) # Get r i d o f l i n e s w i t h NaN v a l u e s VLSSr = VLSSr_raw [ np. isnan ( VLSSr_raw ). any ( axis=1) ] # Read t h e columns w i t h t h e data # v a l u e s we ' r e i n t e r e s t e d i n VLSSr_RA = VLSSr [ :, : 3 ] VLSSr_Dec = VLSSr [ :, 3 : 6 ] # F l u x and f l u x e r r o r s a r e i n Jy VLSSr_flux = VLSSr [ :, 6 ] [ np. newaxis ]. transpose ( ) VLSSr_flux_err = VLSSr_raw [ np. argwhere ( np. isnan ( VLSSr_raw [ :, 0 ] ) ) [ :, 0 ] ] [ :, 1] VLSSr_flux_err = VLSSr_flux_err [ np. newaxis ]. transpose ( ) # Convert t h e RA and Dec # c o o r d i n a t e s i n d e g r e e s u s i n g A s t r o p y VLSSr_ras_deg = [ ] VLSSr_decs_deg = [ ] f o r i i n r a n g e ( l e n ( RA ) ) : ra = ( s t r ( i n t ( VLSSr_RA [ i ] [ 0 ] ) )+ ' h '+ s t r ( i n t ( VLSSr_RA [ i ] [ 1 ] ) )+ 'm'+ s t r ( VLSSr_RA [ i ] [ 2 ] ) + ' s ' ) dec = ( s t r ( i n t ( VLSSr_Dec [ i ] [ 0 ] ) )+ ' d '+ s t r ( i n t ( VLSSr_Dec [ i ] [ 1 ] ) )+ 'm'+ s t r ( VLSSr_Dec [ i ] [ 2 ] ) + ' s ' )

81 c = SkyCoord ( ra, dec, frame= ' f k 5 ' ) VLSSr_ras_deg. append ( c. ra. deg ) VLSSr_decs_deg. append ( c. dec. deg ) VLSSr_ras_deg = np. array ( VLSSr_ras_deg ) [ np. newaxis ]. transpose ( ) VLSSr_decs_deg = np. array ( VLSSr_decs_deg ) [ np. newaxis ]. transpose ( ) # Combine t h e data i n t o # a s i n g l e numpy a r r a y VLSSr_data = np. concatenate ( ( VLSSr_ras_deg, VLSSr_decs_deg, VLSSr_flux, VLSSr_flux_err ), axis=1) # Write t h e data to a c s v f i l e header = ( 'RA ( deg J2000 ), Dec ( deg J2000 ), ' ' F l u x ( Jy ), F l u x e r r o r ( Jy ) ' ) np. savetxt ( ' V L S S r p r o c e s s e d. c s v ', VLSSr_data, delimiter= ', ', header=header ) # Import t h e WSRT data u s i n g # s p e c i f i c column s i z e s WSRT_original = np. genfromtxt ( WSRT_file, comments= '#', delimiter =(12, 4, 2, 7, 6, 3, 3, 4, 5, 6, 4, 3, 5, 5, 8, 7), usecols =( 2, 1, 9, 10) ) # Convert t h e f l u x measurements from # mjy to Jy WSRT_flux = WSRT_original [ :, 2 ] 1 e 3 WSRT_flux = WSRT_flux [ np. newaxis ]. transpose ( ) WSRT_flux_err = WSRT_original [ :, 3 ] 1 e 3 WSRT_flux_err = WSRT_flux_err [ np. newaxis ]. transpose ( ) # Get out t h e c o o r d i n a t e i n f o r m a t i o n # i n G a l a c t i c c o o r d i n a t e s WSRT_l = WSRT_original [ :, 0 ] WSRT_b = WSRT_original [ :, 1 ] # C onvert t h e G a l a c t i c c o o r d i n a t e s # to J2000 RA and Dec i n d e g r e e s WSRT_ras_deg = [ ] WSRT_decs_deg = [ ] f o r i i n r a n g e ( l e n ( WSRT_l ) ) : l = ( WSRT_l [ i ] ) b = ( WSRT_b [ i ] ) gc = SkyCoord ( l=l u. degree, b=b u. degree, frame= ' g a l a c t i c ' ) c = gc. transform_to ( ' f k 5 ' ) WSRT_ras_deg. append ( c. ra. deg )

82 WSRT_decs_deg. append ( c. dec. deg ) WSRT_ras_deg = np. array ( WSRT_ras_deg ) [ np. newaxis ]. transpose ( ) WSRT_decs_deg = np. array ( WSRT_decs_deg ) [ np. newaxis ]. transpose ( ) # Combine t h e WSRT data i n t o a # s i n g l e a r r a y WSRT_data = np. concatenate ( ( WSRT_ras_deg, WSRT_decs_deg, WSRT_flux, WSRT_flux_err ), axis=1) # Write t h e data to a c s v f i l e header = ( 'RA ( deg J2000 ), Dec ( deg J2000 ), ' ' F l u x ( Jy ), F l u x e r r o r ( Jy ) ' ) np. savetxt ( ' WSRT processed. c s v ', WSRT_data, delimiter= ', ', header=header ) B.2 Running pybdsf and aperture flux density measurements Listing B.2: Small script to run pybdsf, particularly aperture photometry with the aperture radius equal to the semi-major axis of the beam of the SB. # S c r i p t to run pybdsf on HBA SB F I T S f i l e s # u s i n g advanced a p e r t u r e o p t i o n i m p o r t bdsf i m p o r t os i m p o r t numpy as np i m p o r t glob i m p o r t time d e f run_bdsf ( briggs, input_dir, results_dir, tp =2.0, beaminfo=beam_info ) : ' ' ' Run pybdsf on HBA F I T S f i l e s. Args : b r i g g s ( s t r ) : b r i g g s w e i g h t i n g i n p u t d i r ( s t r ) : path to i n p u t f i l e s r e s u l t s d i r ( s t r ) : path to o u t p u t l o c a t i o n kwargs : tp ( f l o a t ) : pybdsf t h r e s h p i x v a l u e beaminfo ( d i c t ) : d i c t i o n a r y c o n t a i n i n g t h e semi major a x e s o f t h e beam o f t h e SBs o f i n t e r e s t. This w i l l be used as t h e a p e r t u r e r a d i u s. ' ' ' infiles = glob. glob ( input_dir + 'SB image. f i t s ' ) f o r image i n infiles :

83 ' ) ] base_filename = image [ image. index ( input_dir )+l e n ( input_dir ) : image. index ( ' image. f i t s SBinfo = image [ image. index ( input_dir )+ l e n ( input_dir ) : image. index ( ' DATA ' ) ] beamsize_pix = beaminfo [ SBinfo ] img = bdsf. process_image ( image, thresh_pix=tp, advanced_opts=true, aperture= beamsize_pix ) img. write_catalog ( format= ' c s v ', outfile=results_dir+base_filename+ '. c s v ' ) img. write_catalog ( format= ' ds9 ', outfile=results_dir+base_filename+ '. r e g ' ) img. export_image ( img_type= ' g a u s r e s i d ', outfile=results_dir+base_filename+ '. r e s i d ' ) img. export_image ( img_type= ' gaus model ', outfile=results_dir+base_filename+ '. model ' ) os. system ( 'mv '+input_dir+ ' '+base_filename+ '. l o g '+results_dir ) i f name i n ' m a i n ' : # Get t h e d a t e and time f o r t h e r e s u l t s f o l d e r datetime = time. strftime ( '%Y.%m.% d %H:%M' ) # Set your B r i g g s v a l u e briggs = ' 1. 0 ' # Choose t h e p i x e l t h r e s h o l d v a l u e tp = 2. 0 # The beam i n f o r m a t i o n d i c t i o n a r y beam_info = { ' SB000 SB010 ' : , ' SB010 SB020 ' : , ' SB020 SB030 ' : , ' SB030 SB040 ' : , ' SB040 SB050 ' : , ' SB050 SB060 ' : , ' SB060 SB070 ' : , ' SB070 SB080 ' : , ' SB080 SB090 ' : , ' SB090 SB100 ' : , ' SB100 SB110 ' : , ' SB110 SB120 ' : , ' SB120 SB130 ' : , ' SB130 SB140 ' : , ' SB140 SB150 ' : , ' SB150 SB160 ' : , ' SB160 SB170 ' : , ' SB170 SB180 ' : , ' SB180 SB190 ' : , ' SB190 SB200 ' : , ' SB200 SB210 ' : , ' SB210 SB220 ' : , ' SB220 SB230 ' : , ' SB230 SB240 ' : , ' SB240 SB250 ' : , ' SB250 SB260 ' : } # Path to t h e F I T S f i l e s input_dir = ' / data1 / d r i e s s e n / H B A f i t s / ' # Path to t h e o u t p u t d i r e c t o r y results_dir = input_dir + datetime+ ' r e s u l t s / ' os. system ( ' mkdir '+results_dir )

84 # Run pybdsf run_bdsf ( briggs, input_dir, results_dir, tp=tp ) B.3 Extracting point source information from TGSS ADR and pybdsf Listing B.3: Script to extract the point source information from the TGSS ADR TSV point source catalogue and the WSRT and VLSSR CSV files produced using the script in Appendix B.1 as well as the point source information from the pybdsf output CSV files. This script also produces DS9 region files to test the point source finding method and plots of flux density against frequency for the detected point sources. params = { f i g u r e. f i g s i z e : ( 1 2, 9 ), f o n t. s i z e : 20, f o n t. w e i g h t : normal, x t i c k. major. s i z e : 9, x t i c k. minor. s i z e : 4, y t i c k. major. s i z e : 9, y t i c k. minor. s i z e : 4, x t i c k. major. width : 4, x t i c k. minor. width : 3, y t i c k. major. width : 4, y t i c k. minor. width : 3, x t i c k. major. pad : 8, x t i c k. minor. pad : 8, y t i c k. major. pad : 8, y t i c k. minor. pad : 8, l i n e s. l i n e w i d t h : 3, l i n e s. m a r k e r s i z e : 10, a x e s. l i n e w i d t h : 4, l e g e n d. l o c : b e s t, t e x t. u s e t e x : False, x t i c k. l a b e l s i z e : 20, y t i c k. l a b e l s i z e : 20, } i m p o r t matplotlib matplotlib. rcparams. update ( params ) i m p o r t os i m p o r t numpy as np i m p o r t matplotlib. pyplot as plt from astropy i m p o r t units as u from astropy. coordinates i m p o r t SkyCoord from astropy. coordinates i m p o r t ICRS, Galactic, FK4, FK5 i m p o r t csv i m p o r t glob i m p o r t aplpy as apl i m p o r t matplotlib. cm as cm i m p o r t time

85 d e f get_pybdsfinfo ( filename, path, end_string= ' n i t e r ' ) : ' ' ' E x t r a c t s a l l s o u r c e i n f o r m a t i o n from pybdsf produced c s v f i l e. Args : f i l e n a m e ( s t r ) : path to and name o f t h e c s v f i l e R e t u r n s : SBinfo ( s t r ) : name o f SBs o f pybdsf f i l e ps ( a r r a y ) : a r r a y c o n t a i n i n g t h e s o u r c e i n f o r m a t i o n. Each row i s d i f f e r e n t s o u r c e, t h e columns a r e : Source ID, RA, Dec, F l u x ( Jy ), F l u x e r r ( Jy ), maj ( deg ), min ( deg ), PA ( r e l a t i v e to G a l a c t i c c o o r d s ) ' ' ' SBinfo = f [ f. index ( path )+ l e n ( path ) : f. index ( end_string ) ] info = np. genfromtxt ( f, comments= '#', skip_header =5, usecols =(2, 4, 6, 8, 9, 16, 18, 20), delimiter= ', ' ) ps = info [ info [ :, 5 ] < 0. 1 ] ps [ :, 1 ] = ps [ :, 1 ] freq = np. ones ( ( l e n ( ps ), 1) ) HBA_freqs [ SBinfo ] ps = np. append ( ps, freq, axis=1) r e t u r n SBinfo, ps d e f read_tgss ( filename, min_ra, max_ra, min_dec, max_dec ) : ' ' ' E x t r a c t s s o u r c e i n f o r m a t i o n from TGSS ADR t s v f i l e. Args : f i l e n a m e ( s t r ) : name o f t h e TGSS ADR t s v f i l e min RA ( f l o a t ) : minimum r i g h t a s c e n s i o n o f o u t p u t p o i n t s o u r c e s i n d e g r e e s max RA ( f l o a t ) : maximum r i g h t a s c e n s i o n o f o u t p u t p o i n t s o u r c e s i n d e g r e e s min Dec ( f l o a t ) : minimum d e c l i n a t i o n o f o u t p u t p o i n t s o u r c e s i n d e g r e e s max Dec ( f l o a t ) : maximum d e c l i n a t i o n o f o u t p u t p o i n t s o u r c e s i n d e g r e e s R e t u r n s : TGSS fov ( a r r a y ) : a r r a y o f s o u r c e i n f o r m a t i o n. Each row i s d i f f e r e n t s o u r c e. Columns a r e : RA, Dec, T o t a l F l u x ( mjy ), F l u x e r r o r ( mjy ), maj ( a r c s e c ), min ( a r c s e c ), PA ' ' ' # Columns r e a d a r e : # RA, Dec, T o t a l F l u x ( mjy ), # F l u x e r r o r ( mjy ), maj ( a r c s e c ), min ( a r c s e c ), PA TGSS_all = np. genfromtxt ( filename, comments= '#', delimiter= ' \ t ',

86 usecols =(1, 3, 5, 6, 9, 11, 13) ) # C onvert t o t a l f l u x and f l u x e r r o r to Jy TGSS_all [ :, 2 ] = TGSS_all [ :, 2 ] 1 e 3 TGSS_all [ :, 3 ] = TGSS_all [ :, 2 ] 1 e 3 TGSS_fov = TGSS_all [ ( TGSS_all [ :, 0 ] > min_ra ) & ( TGSS_all [ :, 0 ] < max_ra ) & ( TGSS_all [ :, 1 ] > min_dec ) & ( TGSS_all [ :, 1 ] < max_dec ) ] r e t u r n TGSS_fov d e f read_csv ( filename, min_ra, max_ra, min_dec, max_dec ) : ' ' ' E x t r a c t s s o u r c e i n f o r m a t i o n from CSV f i l e s. CSV f i l e s h o u l d c o n s i s t o f f o u r columns, RA ( d e g r e e s ), Dec ( d e g r e e s ), F l u x ( Jy ), F l u x e r r o r ( Jy ) Args : f i l e n a m e ( s t r ) : name o f t h e c s v f i l e min RA ( f l o a t ) : minimum r i g h t a s c e n s i o n o f o u t p u t p o i n t s o u r c e s i n d e g r e e s max RA ( f l o a t ) : maximum r i g h t a s c e n s i o n o f o u t p u t p o i n t s o u r c e s i n d e g r e e s min Dec ( f l o a t ) : minimum d e c l i n a t i o n o f o u t p u t p o i n t s o u r c e s i n d e g r e e s max Dec ( f l o a t ) : maximum d e c l i n a t i o n o f o u t p u t p o i n t s o u r c e s i n d e g r e e s R e t u r n s : PS fov ( a r r a y ) : a r r a y o f s o u r c e i n f o r m a t i o n. Each row i s d i f f e r e n t s o u r c e. Columns a r e : RA, Dec, T o t a l F l u x ( mjy ), F l u x e r r o r ( mjy ), f r e q u e n c y (Mhz) ' ' ' # Columns r e a d a r e : # RA, Dec, T o t a l F l u x ( mjy ), F l u x e r r o r ( mjy ) all_data = np. genfromtxt ( filename, comments= '#', delimiter= ', ' ) PS_fov = all_data [ ( all_data [ :, 0 ] > min_ra ) & ( all_data [ :, 0 ] < max_ra ) & ( all_data [ :, 1 ] > min_dec ) & ( all_data [ :, 1 ] < max_dec ) ] r e t u r n PS_fov d e f write_regfile ( filename, lines ) : ' ' ' W r i t e s DS9 r e g i o n f i l e. Args : f i l e n a m e ( s t r ) : f i l e n a m e o f o u t p u t r e g i o n f i l e ( Should i n c l u d e. r e g e x t e n s i o n ) l i n e s ( l i s t ) : i n f o r m a t i o n f o r each r e g i o n. Should

87 ' ' ' be i n s t a n d a r d DS9 r e g i o n format all_lines = [ ( '# Region f i l e format : ' ' DS10 v e r s i o n 4. 1 ' ), ( ' g l o b a l c o l o r=g r e e n f o n t= ' ' \ h e l v e t i c a 10 normal \ ' ' s e l e c t =1 h i g h l i t e =1 e d i t =1 ' ' move=1 d e l e t e =1 ' ' i n c l u d e =1 f i x e d =0 s o u r c e ' ), ' f k 5 ' ] f o r line i n lines : all_lines. append ( line ) thefile = open ( filename, 'w ' ) f o r line i n all_lines : thefile. write ( %s \n % line ) thefile. close ( ) i f name i n ' m a i n ' : # F r e q u e n c i e s ( i n MHz) o f t h e # summed MSs HBA_freqs = { ' SB000 SB010 ' : , ' SB010 SB020 ' : , ' SB020 SB030 ' : , ' SB030 SB040 ' : , ' SB040 SB050 ' : , ' SB050 SB060 ' : , ' SB060 SB070 ' : , ' SB070 SB080 ' : , ' SB080 SB090 ' : , ' SB090 SB100 ' : , ' SB100 SB110 ' : , ' SB110 SB120 ' : , ' SB120 SB130 ' : , ' SB130 SB140 ' : , ' SB140 SB150 ' : , ' SB150 SB160 ' : , ' SB160 SB170 ' : , ' SB170 SB180 ' : , ' SB180 SB190 ' : , ' SB190 SB200 ' : , ' SB200 SB210 ' : , ' SB210 SB220 ' : , ' SB220 SB230 ' : , ' SB230 SB240 ' : , ' SB240 SB250 ' : , ' SB250 SB260 ' : , ' SB010 SB070 ' : , ' SB070 SB130 ' : , ' SB130 SB190 ' : , ' SB190 SB250 ' : , ' SB000 SB260 ' : }

88 # Get t h e names o f t h e pybdsf c s v f i l e s pybdsfpath = ( ' /home/ l a u r a / Documents / M s c P r o j e c t / p y b d s f / ' ) #( ' / home/ l a u r a / Documents / M s c P r o j e c t / p y b d s f / a p e r t u r e t a k e 2 p y b d s f f i l e s / ' ) pybdsfcsvs = glob. glob ( ( pybdsfpath+ 'SB SB. c s v ' ) ) # path to t h e o u t p u t f o l d e r dt = time. strftime ( '%Y.%m.% d %H:%M' ) outputpath = ( ' /home/ l a u r a / Documents / M s c P r o j e c t / ' ' p y b d s f / '+dt+ ' o u t p u t / ' ) os. system ( ' mkdir '+outputpath ) # Read t h e TGSS, VLSSr and WSRT c a t a l o g u e s TGSS = read_tgss ( ( ' /home/ l a u r a / Documents / M s c P r o j e c t / ' ' TGSSADR1 7sigma catalog. t s v ' ), 286, 299, 13, 25) VLSSr = read_csv ( ( ' /home/ l a u r a / Documents / ' ' M s c P r o j e c t / V L S S r p r o c e s s e d. c s v ' ), 286, 299, 13, 25) WSRT = read_csv ( ( ' /home/ l a u r a / Documents / ' ' M s c P r o j e c t / WSRT processed. c s v ' ), 286, 299, 13, 25) # Read t h e pybdsf c s v f i l e s and w r i t e # s o u r c e i n f o r m a t i o n to a d i c t i o n a r y ps_info = d i c t ( ) f o r i, f i n enumerate ( pybdsfcsvs ) : SBinfo, ps = get_pybdsfinfo ( f, pybdsfpath ) ps_info [ SBinfo ] = ps # Set t h e a c c e p t a b l e d i s t a n c e between # t h e c e n t r e o f t h e TGSS s o u r c e and t h e # c e n t r e o f t h e pybdsf s o u r c e dist = # Search t h e pybdsf s o u r c e s ( f o r each SB) f o r # s o u r c e s matching t h e TGSS p o i n t s o u r c e s # and w r i t e t h e s o u r c e s to a d i c t i o n a r y PSs = d i c t ( ) PS = 0 # Go t h r o u g h each TGSS s o u r c e s f o r test_ps i n TGSS : # Check each SB pybdsf c a t a l o g u e f o r SBname, PSarray i n ps_info. iteritems ( ) : pss = PSarray [ ( PSarray [ :, 1 ] < test_ps [0]+ dist ) & ( PSarray [ :, 1 ] > test_ps [0] dist ) & ( PSarray [ :, 2 ] < test_ps [1]+ dist ) & ( PSarray [ :, 2 ] > test_ps [1] dist ) ] i f l e n ( pss ) == 1 : t r y : PSinfo = PSs [ PS ] pss = l i s t ( pss [ 0 ] ) PSinfo. append ( pss ) PSs [ PS ] = PSinfo e x c e p t KeyError : pss = l i s t ( pss [ 0 ] )

89 PS += 1 PSs [ PS ] = [ pss ] # E x t r a c t t h e p o i n t s o u r c e s t h a t a r e # found i n a t l e a s t 15 HBA SBs plentyofpoints = [ ] f o r key, PS i n PSs. iteritems ( ) : i f l e n ( PS ) > 1 5 : plentyofpoints. append ( key ) # Write t h e s e p o i n t s o u r c e s to a r e g i o n f i l e lines = [ ] f o r PSkey i n plentyofpoints : PS = np. array ( PSs [ PSkey ] ) major = s t r ( np. amax ( PS [ :, 5 ] ) ) minor = s t r ( np. amax ( PS [ :, 6 ] ) ) PA = ' 0. 0 ' RA = s t r ( TGSS [ PSkey ] [ 0 ] ) Dec = s t r ( TGSS [ PSkey ] [ 1 ] ) t = s t r ( PSkey ) line = ( ' e l l i p s e ( '+RA+ ', '+Dec+ ', '+major+ ' \, '+minor+ ' \, 0. 0 ) # c o l o r=g r e e n width=1 t e x t ={ '+ t+ ' } ' ) lines. append ( line ) write_regfile ( outputpath+ ' p l e n t y o f p o i n t s. r e g ', lines ) # The p o i n t s o u r c e s found by pybdsf can # be c o n f u s e d i f t h e TGSS s o u r c e s a r e c l o s e # t o g e t h e r. Remove any p o i n t s o u r c e s t h a t # a r e w i t h i n 3 d i s t o f a n o t h e r TGSS p o i n t s o u r c e TGSSpops = TGSS [ plentyofpoints ] safedist = 0. 1 goodsources = [ ] f o r i, source i n enumerate ( TGSSpops ) : ra = source [ 0 ] dec = source [ 1 ] tooclose = TGSS [ ( TGSS [ :, 0 ] < ra+safedist ) & ( TGSS [ :, 0 ] > ra safedist ) & ( TGSS [ :, 1 ] < dec+safedist ) & ( TGSS [ :, 1 ] > dec safedist ) ] i f l e n ( tooclose ) == 1 : goodsources. append ( plentyofpoints [ i ] ) # Search t h e VLSSr c a t a l o g u e f o r t h e # good p o i n t s o u r c e s VLSSr_dist = 1 0. / 6 0. / 6 0. VLSSr_goodsources = d i c t ( ) f o r PSkey i n goodsources : PS = np. array ( PSs [ PSkey ] ) ra = TGSS [ PSkey ] [ 0 ] dec = TGSS [ PSkey ] [ 1 ]

90 good = VLSSr [ ( VLSSr [ :, 0 ] < ra+vlssr_dist ) & ( VLSSr [ :, 0 ] > ra VLSSr_dist ) & ( VLSSr [ :, 1 ] < dec+vlssr_dist ) & ( VLSSr [ :, 1 ] > dec VLSSr_dist ) ] i f l e n ( good ) == 1 : VLSSr_goodsources [ PSkey ] = good [ 0 ] # Search t h e WSRT c a t a l o g u e f o r t h e # good p o i n t s o u r c e s WSRT_dist = 1 0. / 6 0. / 6 0. WSRT_goodsources = d i c t ( ) f o r PSkey i n goodsources : PS = np. array ( PSs [ PSkey ] ) ra = TGSS [ PSkey ] [ 0 ] dec = TGSS [ PSkey ] [ 1 ] good = WSRT [ ( WSRT [ :, 0 ] < ra+wsrt_dist ) & ( WSRT [ :, 0 ] > ra WSRT_dist ) & ( WSRT [ :, 1 ] < dec+wsrt_dist ) & ( WSRT [ :, 1 ] > dec WSRT_dist ) ] i f l e n ( good ) == 1 : WSRT_goodsources [ PSkey ] = good [ 0 ] # Write t h e s e p o i n t s o u r c e s to a r e g i o n f i l e lines = [ ] f o r PSkey i n goodsources : PS = np. array ( PSs [ PSkey ] ) major = s t r ( np. amax ( PS [ :, 5 ] ) ) minor = s t r ( np. amax ( PS [ :, 6 ] ) ) PA = ' 0. 0 ' RA = s t r ( TGSS [ PSkey ] [ 0 ] ) Dec = s t r ( TGSS [ PSkey ] [ 1 ] ) t = s t r ( PSkey ) line = ( ' e l l i p s e ( '+RA+ ', '+Dec+ ', '+major+ ' \, '+minor+ ' \, 0. 0 ) # c o l o r=w h i t e width=4 t e x t ={ '+ t+ ' } ' ) lines. append ( line ) write_regfile ( outputpath+ ' g o o d s o u r c e s. r e g ', lines ) # P l o t t h e f l u x d e n s i t i e s o f t h e p o i n t s o u r c e s # ( The TGSS, VLSSr, WSRT and LOFAR f l u x d e n s i t i e s ) f o r PSkey i n goodsources : fig, ax = plt. subplots ( 1, 1, figsize =(10, 6) ) PS = np. array ( PSs [ PSkey ] ) ax. errorbar ( PS [ :, 1], PS [ :, 3 ], yerr=ps [ :, 4 ], fmt= ' o ', color= '#FF3399 ', label= 'LOFAR HBA ' ) ax. errorbar ( 1 5 0, TGSS [ PSkey ] [ 2 ], yerr=tgss [ PSkey ] [ 3 ], fmt= ' o ', color= ' #0033FF ', label= 'TGSS ' )

91 t r y : ax. errorbar ( 7 4, VLSSr_goodsources [ PSkey ] [ 2 ], yerr=vlssr_goodsources [ PSkey ] [ 3 ], fmt= ' o ', color= ' #6600CC ', label= ' VLSSr ' ) e x c e p t KeyError : p r i n t ' VLSSr not d e t e c t e d : ', PSkey t r y : ax. errorbar ( 3 2 7, WSRT_goodsources [ PSkey ] [ 2 ], yerr=wsrt_goodsources [ PSkey ] [ 3 ], fmt= ' o ', color= ' # ', label= 'WSRT ' ) e x c e p t KeyError : p r i n t 'WSRT not d e t e c t e d : ', PSkey ax. set_title ( ' P o i n t s o u r c e '+s t r ( PSkey ), fontsize =25) ax. set_xlabel ( ' Frequency ( Hz ) ', fontsize =24) ax. set_ylabel ( ' F l u x d e n s i t y ( Jy ) ', fontsize =24) ax. legend ( frameon=false, fontsize =20) i f PSkey < 1 0 : sourcename = ' 000 '+s t r ( PSkey ) e l i f PSkey < : sourcename = ' 00 '+s t r ( PSkey ) e l i f PSkey < : sourcename = ' 0 '+s t r ( PSkey ) e l s e : sourcename = s t r ( PSkey ) ax. set_xlim ( 5 0, 350) ax. set_ylim ( 0, 3. 5 ) RA = s t r ( TGSS [ PSkey ] [ 0 ] ) Dec = s t r ( TGSS [ PSkey ] [ 1 ] ) plt. tight_layout ( ) plt. savefig ( outputpath+ ' RAandDec/ '+ ' PS '+sourcename+ ' RA '+ RA+ ' Dec '+Dec+ '. png ' ) plt. close ( )

92 Appendix C Searching for a pulsar in G a new supernova remnant detected with LOFAR Scientific abstract: We have discovered a new supernova remnant (SNR) using LOFAR. G has a shell-like morphology, a negative spectral index and associated thermal X-ray emission. We propose to use Arecibo to search for a young pulsar associated with G Such a pulsar would be a powerful probe of the SNR itself, and would also provide valuable insights into the preceding supernova explosion, and the birth of the neutron star. Popular abstract: Pulsars, i.e. rotating neutron stars, are born when massive stars explode as supernovae. We have discovered evidence of such an explosion, a supernova remnant, in the Milky Way using the LOw Frequency ARray (LOFAR) in the Netherlands. We would like to search the remnant to investigate whether this explosion produced a detectable pulsar. Arecibo is an extremely sensitive instrument perfect for searching for previously undetected pulsars. We serendipitously discovered supernova remnant (SNR) G while investigating another object, pulsar wind nebula (PWN) G , using the LOw Frequency ARray (LOFAR) and High-Band Antenna (HBA) observations. G is not included in Green s SNR catalogue (Green, 2014) nor is it a known HII region in the Wide-Field Infrared Survey Explorer (WISE) catalogue (Anderson et al., 2014). It was detected as a source, but not an SNR, by Day et al. (1972) in a Galactic Plane survey at 2.7 GHz. There is no known pulsar near G Figure C.1 shows the image that led to the identification of G as an SNR candidate. G immediately jumps out as a shell or bubble with an extremely similar colour to well-known SNR HC 40. SNRs have a radio spectral index of α < 0; where F ν ν α for F ν the flux density in Jy and ν the 75

93 frequency in Hz (Onić, 2013). This means that they are brighter at lower frequencies, or red in Figure C.1, like both HC 40 and G HII regions instead have a negative spectral index, α 0, and appear brighter at higher frequencies, or blue in Figure C.1. Subsequently, we discovered that ROentgen SATellite (ROSAT) and X-ray Multi-Mirror Mission (XMM- Newton) data show that G has associated extended X-ray emission with a similar morphology to the radio emission. G is on the edge of the XMM-Newton field of view, but the X-ray spectrum is thermal with Mg, Si and S line emission from a hot plasma. The plasma is out of equilibrium and has a lower ionisation age, indicating a young source. These X-ray characteristics show conclusively that G is a SNR. The radio and X-ray observations indicate that G is a SNR. If there is a pulsar associated with G this would open many possibilities for deeper study. Indeed, such associations are still rare, and highly valuable for understanding supernovae and the births of young neutron stars. G is roughly circular, centred on a right-ascension of 19h29m57.3s and declination of +18d09m54.9s with a radius of 6 arcmin. The closest known pulsar to G is roughly half a degree away, and thus very unlikely to be associated. Nearby PWN G , also located in the Perseus arm of the Milky Way, has a central pulsar (PSR J ; P = 136 ms; DM=308 pc cm 3 ) that was discovered using the Wide-band Arecibo Pulsar Processor (WAPP) at 1175MHz by Camilo et al. (2002). They used the 100 MHz 3-level mode of the WAPP and observed for 2.7 hours. We aim to again use Arecibo s uniquely high sensitivity to discover another young pulsar in this region of the Galaxy. Given the 6 arcmin extent of G and the possibility that the putative pulsar could be at the edge of the remnant, we will need to use the Arecibo L-band Feed Array (ALFA) which, with 3 interleaved pointings, can cover the required region. The data will be recorded with the Mock spectrometers in a standard pulsar search mode. We request two epochs of 1.5 hrs (including setup and test pulsar) in order to do 3 20 min tessellated ALFA pointings in each case. Between the two epochs, we will offset the 3-pointing ALFA grid by half a beam width in order to achieve close to Nyquist sampling of the search region to be robust against the pulsar falling at the half power point of an ALFA beam. This search setup will allow us to detect a 36 µjy pulsar with a S/N of 15. In other words, we can easily find a pulsar that is at least half as bright as the already weak PSR J (where S µjy). We are requesting these data as a short proposal because we are preparing a paper presenting the radio/xray discovery of G and would very much like to include pulsation searches (and potentially a pulsar discovery) as part of that analysis.

94 Figure C.1: A three-colour image of the Galactic Plane showing G (circled in dashed white), SNR HC 40 and PWN G (both circled in white). The three colours in this image are: LOFAR HBA 150 MHz in red, Westerbork Synthesis Radio Telescope (WSRT) 327 MHz in green and Very Large Array (VLA) Galactic Plane Survey (VGPS) 1400 MHz in blue. The red, yellow and green circles are known, radio quiet and group HII regions respectively from the WISE catalogue (Anderson et al., 2014).