Bayesian Strong Gravitational-Lens Modeling on Adaptive Grids: Objective Detection of Mass Substructure in Galaxies
S. Vegetti (Kapteyn), L.V.E. Koopmans (Kapteyn)

TL;DR
This paper presents a Bayesian grid-based method for modeling strong gravitational lenses to detect and quantify luminous and dark-mass substructures in galaxies, demonstrated on simulated data with promising results.
Contribution
The paper introduces an adaptive Bayesian grid-based approach with nested sampling for objective detection of galaxy substructures in gravitational lensing.
Findings
Able to identify substructures with masses >=10^7 solar mass on Einstein rings
Quantifies errors on non-linear mass model parameters
Ranks potential models based on marginalized evidence
Abstract
We introduce a new adaptive and fully Bayesian grid-based method to model strong gravitational lenses with extended images. The primary goal of this method is to quantify the level of luminous and dark-mass substructure in massive galaxies, through their effect on highly-magnified arcs and Einstein rings. The method is adaptive on the source plane, where a Delaunay tessellation is defined according to the lens mapping of a regular grid onto the source plane. The Bayesian penalty function allows us to recover the best non-linear potential-model parameters and/or a grid-based potential correction and to objectively quantify the level of regularization for both the source and the potential. In addition, we implement a Nested-Sampling technique to quantify the errors on all non-linear mass model parameters -- ... -- and allow an objective ranking of different potential models in terms of…
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