Nightingale, J. W. and Dye, S. and Massey, R. J. (2018) 'AutoLens : automated modeling of a strong lens's light, mass, and source.', Monthly notices of the Royal Astronomical Society., 478 (4). pp. 4738-4784.
This work presents AutoLens, the first entirely automated modeling suite for the analysis of galaxy-scale strong gravitational lenses. AutoLens simultaneously models the lens galaxy’s light and mass whilst reconstructing the extended source galaxy on an adaptive pixel-grid. The method’s approach to source-plane discretization is amorphous, adapting its clustering and regularization to the intrinsic properties of the lensed source. The lens’s light is fitted using a superposition of Sersic functions, allowing AutoLens to cleanly deblend its light from the source. Single-component mass models representing the lens’s total mass density profile are demonstrated, which in conjunction with light modeling can detect central images using a centrally cored profile. Decomposed mass modeling is also shown, which can fully decouple a lens’s light and dark matter and determine whether the two components are geometrically aligned. The complexity of the light and mass models is automatically chosen via Bayesian model comparison. These steps form AutoLens’s automated analysis pipeline, such that all results in this work are generated without any user intervention. This is rigorously tested on a large suite of simulated images, assessing its performance on a broad range of lens profiles, source morphologies, and lensing geometries. The method’s performance is excellent, with accurate light, mass, and source profiles inferred for data sets representative of both existing Hubble imaging and future Euclid wide-field observations.
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|Publisher Web site:||https://doi.org/10.1093/mnras/sty1264|
|Publisher statement:||This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society © 2018 The Authors. Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.|
|Record Created:||12 Jul 2018 12:28|
|Last Modified:||21 Sep 2018 12:38|
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