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Discovery and Characterisation of Gravitationally Lensed Quasars in Wide-Field Surveys

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Discovery and Characterisation of Gravitationally Lensed Quasars in Wide-Field Surveys Discovery and Characterisation of Gravitationally Lensed Quasars in Wide-field Surveys CAMERON LEMON Supervisors: RICHARD MCMAHON and MATTHEW AUGER Institute of Astronomy University of Cambridge This dissertation is submitted for the degree of Doctor of Philosophy Corpus Christi College May 2019 Declaration This dissertation is the result of my own work and includes nothing which is the outcome of work done in collaboration except as declared in the Preface and specified in the text. Itis not substantially the same as any that I have submitted, or, is being concurrently submitted for a degree or diploma or other qualification at the University of Cambridge or any other University or similar institution. It does not exceed 60,000 words, including abstract, tables, footnotes and appendices. CAMERON LEMON May 2019 Acknowledgements I am immensely grateful to both my supervisors, Richard McMahon and Matt Auger. Thank you for your patience. I appreciated every email, office visit, and tea-time conversation. I’d also like to thank Fernanda Ostrovski, Sophie Reed, and Lindsay Oldham for their generous support throughout my PhD. Thank you to everyone at the IoA for always being supportive and keen to help me with my research. Special thanks go to Manda Banerji, Elmé Breedt, Paul Hewett, and Sergey Koposov. Outside of the IoA, I am thankful to everyone I’ve met through the STRIDES collaboration for their thoughts, motivation, and help over the past few years. In particular, I’d like to thank Paul Schechter for his time and advice. Thank you to all my officemates and other students for making time in the office enjoyable every day. I’d particularly like to thank Andrew, Aneesh, and Pablo, for all the great times inside and outside the office. Finally, thank you to my parents, my sisters, my grandma, and Elisabeth for all the unconditional love and support you’ve given me. I have you all to thank for where I am today. Abstract The coincident alignment of two galaxies on the sky can create the rare cosmic phenomenon of strong gravitational lensing, in which light from the more distant galaxy is bent around the foreground galaxy to create multiple, distorted, and magnified images. When the background galaxy hosts a bright active galactic nucleus, a quasar, the system becomes a probe of accretion disk physics, quasar-host galaxy relations, the Hubble constant, the stellar IMF, smooth matter fractions, amongst many other applications. It has been 40 years since the discovery of the first gravitationally lensed quasar, and dedicated spectroscopic and imaging surveys have added over one hundred new systems to this list. In recent years, the amount of available data across the whole-sky has grown exponentially. Full-sky data from X-ray to radio wavelengths exist, and predictions suggest there are many bright lensed quasars hidden in these datasets. This thesis presents several new techniques to mine these rare systems from whole-sky photometric datasets. We use the excellent resolving ability of Gaia, coupled with other wide-field surveys such as the Dark Energy Survey (DES), Pan-STARRS, and WISE, and present spectroscopic follow-up from the WHT, NTT, and Keck. By looking for multiple Gaia detections around photometric quasar candidates, and single Gaia detections near morphological galaxies, we have discovered 105 new lensed quasars. We also present a search based on significant offsets in astrometry and flux between Gaia and SDSS for spectroscopic quasars, suggesting several promising small-separation lens candidates. We characterise the confirmed systems based on ground-based imaging and the spatially resolved spectra, and comment on the purity, efficiency, and biases in our selection. DES data provides multi-epoch photometry overthe baseline of years at optical wavelengths, allowing a colour-independent selection of lensed quasars by looking for nearby variable pairs. We create a parametric modelling pipeline of the DES images to extract lightcurves of system components, and show that it is a highly effective way to remove quasar and star projections before spectroscopic follow-up. We demonstrate that future searches based on detecting variability in multiple images will be biased towards four-image lensed quasars. Table of contents List of figures xiii List of tables xvii 1 Introduction1 1.1 Quasars . .1 1.2 Gravitational Lensing . .2 1.2.1 Lensing Formalism . .3 1.2.2 Applications of Lensing . .6 1.3 Lensed Quasar Discovery Surveys . .9 1.3.1 HST snapshot surveys . .9 1.3.2 CLASS/JVAS . 10 1.3.3 SQLS/BQLS . 10 1.3.4 MUSCLES . 11 1.3.5 STRIDES . 11 1.3.6 Other search techniques . 11 1.4 Wide-field Surveys . 12 1.4.1 Pan-STARRS . 12 1.4.2 DES . 13 1.4.3 Gaia ................................. 15 1.4.4 WISE . 18 1.5 Predictions . 19 1.6 Thesis Outline . 23 2 Lensed Quasars from Gaia Data Release 1 25 2.1 Known Lensed Quasars in GDR1 . 25 2.2 Pan-STARRS . 27 2.2.1 Photometric quasar candidate catalogues . 27 x Table of contents 2.2.2 Morphology Selection . 28 2.2.3 Final Lens Candidate Catalogue . 29 2.2.4 Observations . 31 2.2.5 Results . 31 2.2.6 Pan-STARRS Modelling . 32 2.3 DES . 36 2.3.1 Selection . 39 2.3.2 Spectroscopy . 41 2.3.3 High resolution imaging . 41 2.3.4 DES Modelling . 46 2.4 Notes on individual systems . 46 2.5 Discussion . 63 2.5.1 Recovering Known Lenses . 63 2.5.2 Comparison to Simulated Lenses . 64 2.6 Conclusions . 69 3 Resolving Small-separation Lensed Quasars 71 3.1 Close pairs in Gaia .............................. 71 3.2 A sample of small-separation lensed quasars . 72 3.3 Finding quasar lenses in Gaia ......................... 75 3.3.1 Multiple Gaia detections . 75 3.3.2 Single Gaia detections . 76 3.4 Lens candidate selection . 82 3.4.1 Multiple detections . 84 3.4.2 Single detections . 84 3.5 Conclusions . 88 4 Lensed Quasars from Gaia Data Release 2 89 4.1 Gaia DR2 data . 90 4.1.1 Detection Rate . 90 4.1.2 Proper Motions . 91 4.1.3 Astrometric Excess Noise . 93 4.1.4 Removing crowded regions . 93 4.2 Lens Selection . 95 4.2.1 Multiple Gaia detections around quasar catalogues . 95 4.2.2 Modelling unWISE pixels . 96 4.2.3 LRGs with Gaia detections . 98 Table of contents xi 4.2.4 Multiple Chandra detections . 101 4.2.5 Final candidate selection . 101 4.3 Results . 102 4.3.1 Modelling . 102 4.3.2 Notes on Individual Systems . 112 4.4 Discussion . 132 4.5 Conclusions . 134 5 Variability from DES 139 5.1 A photometry pipeline for lensed quasars . 140 5.2 Example Application: ULASJ2343-0050 . 142 5.3 Variability of DES systems . 146 5.3.1 Removing stellar contaminants . 146 5.3.2 J0235-2433 . 150 5.3.3 Nearly Identical Quasar Pairs . 150 5.4 Variability Selection Bias . 152 5.5 Conclusions . 155 6 Discussion and Conclusions 157 6.1 Comparison to Known Lenses . 157 6.2 Comparison to Mock Lenses . 161 6.3 Prospects for Future Searches . 165 6.3.1 Gaia ................................. 165 6.3.2 Variability . 166 6.3.3 Machine Learning . 166 6.3.4 Other Datasets . 167 References 169 Appendix A Lens Photometry and Astrometry 183 Appendix B Inconclusive/Contaminant Systems 191 List of figures 1.1 Schematic of strong gravitational lensing . .4 1.2 Caustics, critical curves, and images for various source positions . .5 1.3 Microlensing caustics, critical curves, and images . .8 1.4 Pan-STARRS and Dark Energy Survey grizY filters. 13 1.5 Dark Energy Survey footprint . 14 1.6 Gaia CCD schematic . 16 1.7 Gaia G, BP, and RP bandpasses . 17 1.8 G–W1 vs W1–W2 for galaxies, quasars, and stars . 19 1.9 Predictions for number of lensed quasars in Pan-STARRS, DES, and Gaia against image separation . 21 1.10 Full sky predictions for number of lensed quasars against magnitude . 22 1.11 Magnitude of combined quasar images against lensing galaxy magnitude in the I-band for the OM10 mock catalogue . 24 2.1 Pan-STARRS gri colour images of Pan-STARRS-selected lensed quasars . 31 2.2 Spectra of Pan-STARRS-selected lensed quasars . 34 2.3 Pan-STARRS gri colour images of Pan-STARRS selected NIQs . 36 2.4 Spectra of NIQs and binaries from Pan-STARRS selection . 39 2.5 Model subtractions of Pan-STARRS-selected lensed quasars . 40 2.6 Spectra of DES-selected lensed quasars . 42 2.7 DES gri colour images of DES-selected lensed quasars . 43 2.8 Spectra of NIQs from DES selection . 44 2.9 NIRC2 AO data and model subtractions for DESJ0245-0556, DESJ0246- 1845, and DESJ0340-2425 . 45 2.10 SOAR data and model subtractions for DESJ0053-2012, DEJ0150-4041, DESJ0407-1931, DESJ0501-4118, and DESJ0600-4649. 47 2.11 PSJ0030-1525 VST data and model subtractions . 48 xiv List of figures 2.12 NIRC2 AO data for PSJ0140+4107, PSJ0417+3325, PSJ0840+3550, and PSJ0949+4208 . 50 2.13 NIRC2 AO data and model subtractions for PSJ0630-1201 . ..
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