A Bayesian Approach to Gravitational Lens Model Selection, SCMAV proceeding

TL;DR

This paper introduces a Bayesian method for selecting gravitational lens models, emphasizing the importance of prior parameter space and demonstrating that simpler models may suffice given current data accuracy.

Contribution

It applies Bayesian model selection to gravitational lens modeling, highlighting the impact of prior assumptions and data quality on model complexity choices.

Findings

More complex lens models are often unjustified with current data accuracy.

Bayes' factors effectively discriminate between competing models.

Simpler models can be sufficient for accurate cosmological inference.

Abstract

Strong gravitational lenses are unique cosmological probes. These produce multiple images of a single source. Whether a single galaxy, a group or a cluster, extracting cosmologically relevant information requires an accurate modeling of the lens mass distribution. A variety of models are available to this purpose, nevertheless discrimination between them as primarely relied on the quality of fit without accounting for the size of the prior model parameter space. This is a problem of model selection that we address in the Bayesian statistics framework by evaluating Bayes' factors. Using simple test cases, we show that the assumption of more complicate lens models may not be justified given the level of accuracy of the available data.