UNIVERSITY of CALIFORNIA Los Angeles the Hubble

UNIVERSITY OF CALIFORNIA Los Angeles The Hubble Constant and the ΛCDM Cosmology: A Magnified View using Strong Lensing A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Astronomy and Astrophysics by Anowar Jaman Shajib 2020 © Copyright by Anowar Jaman Shajib 2020 ABSTRACT OF THE DISSERTATION The Hubble Constant and the ΛCDM Cosmology: A Magnified View using Strong Lensing by Anowar Jaman Shajib Doctor of Philosophy in Astronomy and Astrophysics University of California, Los Angeles, 2020 Professor Tommaso Treu, Chair Recently, a significant tension has been reported between two measurements of the Hubble constant (H0) from early-Universe (e.g., cosmic microwave background) and late-Universe probes (e.g., cosmic distance ladder). If systematic errors are ruled out in these measurements, then new physics extending the ΛCDM model will be required to resolve the tension. Therefore, different independent probes of H0 { such as the strong-lensing time-delay { are essential to confirm or resolve the tension. The measured time-delays between the lensed images of a background quasar constrain H0, as they depend on the absolute physical distances in the lens configuration. I led a team from the STRong-lensing Insights into the Dark Energy Survey (STRIDES) collaboration to analyze the lens DES J0408-5354. I modeled the mass distribution of this lens using Hubble Space Telescope imaging, and combined it +2:7 −1 −1 with analyses from my collaborators to infer H0 = 74:2−3:0 km s Mpc with the highest precision (3.9 per cent) from a single lens to date. This measurement agrees well with both the previous sample of six lenses from the H0LiCOW collaboration and other late-Universe ii probes, thus it increases the aforementioned tension. To confirm or resolve this tension at the 5σ level { the gold standard of detecting new physics { we need to increase the sample size and improve precision per system while keeping the systematics under control. The large amount of required investigator time (∼1 year per lens) is currently the main bottle- neck to increase the sample size. I present an automated lens-modeling framework that will enable rapid increment of the sample size in the near future. I also show, through simula- tion, that incorporating the spatially resolved kinematics of the lensing galaxy improves the precision of H0 per system. Additionally, I develop the first general method to efficiently compute the lensing properties of any given elliptical mass distribution. By allowing any radial shape of mass profile, this method helps to avoid any systematic that may potentially arise from adopting only a few specific parameterizations. I forecast that a sample of ∼40 lenses with spatially resolved kinematics will provide sub-per-cent precision in H0 within the next decade. iii The dissertation of Anowar Jaman Shajib is approved. Edward L. Wright James Larkin Andrea Ghez Tommaso Treu, Committee Chair University of California, Los Angeles 2020 iv To my parents, Anowara Sarker and Abu Naser v TABLE OF CONTENTS 1 Introduction :::::::::::::::::::::::::::::::::::::: 1 1.1 The Λ cold dark matter model of cosmology: concordance and tensions . 1 1.2 Strong gravitational lensing time delays as a cosmological probe . 3 1.3 Overview of this dissertation . 6 2 An evidence for the ΛCDM cosmology: measuring the integrated Sachs{ Wolfe effect :::::::::::::::::::::::::::::::::::::::: 8 2.1 Background . 8 2.2 The ISW Effect . 10 2.3 Data and Methods . 14 2.3.1 CMB Map . 14 2.3.2 WISE Data . 15 2.3.3 Mask . 16 2.3.4 Theoretical Computation . 16 2.4 Results . 17 2.4.1 Redshift Distribution . 17 2.4.2 Bias Measurement . 18 2.4.3 Cross-correlation Measurement . 25 2.5 Discussion and Conclusions . 30 3 Measurement of the Hubble constant from the strong lens system DES J0408−5354 :::::::::::::::::::::::::::::::::::::::: 34 vi 3.1 Background . 35 3.2 Framework of the cosmographic analysis . 40 3.2.1 Strong-lensing time delay . 40 3.2.2 Mass-sheet degeneracy . 42 3.2.3 Kinematic analysis . 44 3.2.4 Cosmological distances . 45 3.2.5 Bayesian inference framework . 46 3.3 The Lens System and Data Sets . 52 3.3.1 HST imaging of the lens system . 53 3.3.2 Spectroscopic observations of the lens components . 54 3.3.3 Time delays . 55 3.3.4 Velocity dispersion of the central deflector . 58 3.3.5 Estimate of the external convergence . 60 3.4 Lens model ingredients . 60 3.4.1 Central deflector’s mass profiles . 60 3.4.2 Central deflector’s light profile . 62 3.4.3 Quasar host galaxy's light profile . 62 3.5 Lens model setups . 63 3.5.1 Baseline models . 63 3.5.2 Central deflector G1's mass and light profiles . 64 3.5.3 Satellite G2's mass and light profile . 66 3.5.4 Nearby line-of-sight galaxies . 67 3.5.5 Galaxy group containing G1 . 74 vii 3.5.6 Source component light profiles . 74 3.5.7 Potential additional image C2 split from the image C . 76 3.5.8 HST image region for likelihood computation . 76 3.5.9 Dust extinction by the satellite G2 . 76 3.5.10 Requirement for astrometric precision . 77 3.5.11 Model choice combinations . 77 3.6 Lens modelling and cosmographic inference . 79 3.6.1 Modelling workflow and results . 79 3.6.2 Combining the time delays, kinematics, and external convergence in- formation . 85 3.6.3 Inference of H0 .............................. 96 3.7 Discussion and summary . 97 3.A Impact of fiducial cosmology in the lens modelling . 102 3.B Impact of likelihood computation region choice . 103 3.C Impact of the convergence from the group containing G1 . 103 3.D Checking for the existence and impact of a dark substructure . 107 3.E Nested sampling settings . 110 3.F Summary of uncertainty budget and systematic checks . 112 3.G Model summary and parameter priors . 112 4 Improving time-delay cosmography with spatially resolved kinematics : 121 4.1 Background . 121 4.2 Model ingredients . 125 4.2.1 Strong gravitational lensing . 125 viii 4.2.2 Deflector mass model . 127 4.3 Creating mock data . 129 4.3.1 Mimicking extended source reconstruction with conjugate points . 132 4.3.2 Mock lensing data with spatially resolved velocity dispersion . 135 4.4 Precision on Cosmological Distance Measurements . 140 4.5 Cosmological inference . 147 4.5.1 Cosmology from strong lensing alone . 147 4.5.2 Joint analysis with Planck . 156 4.6 Discussion and Comparison with Previous Work . 157 4.7 Limitation of this present work . 160 4.8 Summary . 162 5 Automated and uniform modelling of lensed quasars :::::::::::: 165 5.1 Background . 165 5.2 HST sample . 170 5.2.1 Data . 171 5.2.2 Quads in the sample . 171 5.3 Lens modelling . 175 5.3.1 Mass profile parameterization . 178 5.3.2 Light profile parameterization . 178 5.3.3 Modelling procedure . 179 5.3.4 Systematics . 184 5.4 Results . 184 5.4.1 Efficiency of the uniform framework . 184 ix 5.4.2 Lens models . 185 5.4.3 Alignment between mass and light distributions . 190 5.4.4 Deviation of flux ratios from macro-model . 196 5.5 Summary and Discussion . 198 5.A Lens light parameters . 204 5.B Convergence, shear and stellar convergence . 205 5.C Time delays . 205 5.D Lens models . 205 6 Unified lensing and kinematic analysis of elliptical mass profiles ::::: 212 6.1 Background . 212 6.2 Decomposing an elliptical profile into concentric Gaussian components . 217 6.2.1 An integral transform for fast Gaussian decomposition . 219 6.2.2 Decomposing a two-dimensional elliptical profile with the one-dimensional transform . 225 6.3 Kinematics of Gaussian components . 227 6.3.1 Axisymmetric case . 228 6.3.2 Spherical case . 229 6.4 Lensing by Gaussian components . 231 6.4.1 Complex formulation of lensing . 232 6.4.2 Lensing by elliptical Gaussian convergence . 233 6.5 Adding it all together: proof of concept . 237 6.6 Conclusion: precision is feasible. 239 6.A Properties of the Integral transform with a Gaussian Kernel . 244 x 6.B Efficient algorithm to compute the Faddeeva function . ..

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