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Title: AutoLens : automated modeling of a strong lens's light, mass and source
Author: Nightingale, James J. N.
ISNI:       0000 0004 6060 9109
Awarding Body: University of Nottingham
Current Institution: University of Nottingham
Date of Award: 2016
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The intricate analysis of a strong gravitational lens is a complex and computationally demanding problem, requiring the lensed source galaxy's extended light profile to be reconstructed simultaneously with non-linear modeling of the lens galaxy's mass and light. When successful, this analysis gives unrivaled insight into dark matter, cosmology and the most distant Universe. However, such studies remain resigned to small samples, simply due to how long this involved analysis takes. To address this, this thesis presents AutoLens, the first automated framework for comprehensive modeling of a strong gravitational lens's light, mass and source. Reconstruction of the lensed source galaxy uses an adaptive pixel-grid, which is derived in a completely stochastic manner such that a unique pixelization is used for every source reconstruction. This removes biases inherent to pixelized methods associated with the discrete nature of the source-plane. Light profile fitting of the lens galaxy is fully integrated into AutoLens, making it the first method to successfully unify modeling of the lens's light, mass and source into one coherent framework. This allows the method to advocate decomposed mass modeling, which treats separately the lens galaxy's light and dark matter. AutoLens is therefore capable of addressing a diverse range of unique science cases, most notably its ability to determine the central density of a lens galaxy's dark matter halo. These features are incorporated into a fully-automated pipeline, such that the analysis requires no input from the user after an initial setup. This pipeline is tested using a suite of simulated strong lens images which span a variety of source morphologies, lens profiles and lensing geometries. Following the completion of AutoLens's development, the method is ready to analyze the hundreds of archival images of strong gravitational lenses that have been amassed over the past decade, and which are still yet to receive a comprehensive lens analysis. With of order one hundred thousand lenses set to be discovered in the next decade, AutoLens's automated philosophy will be paramount to making analysis of the incoming strong lens samples feasible.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: QB Astronomy