A Bayesian Approach to Gravitational Lens Model Selection, SF2A
  proceeding
  Over the past decade advancements in the understanding of several
astrophysical phenomena have allowed us to infer a concordance cosmological
model that successfully accounts for most of the observations of our universe.
This has opened up the way to studies that aim to better determine the
constants of the model and confront its predictions with those of competing
scenarios. Here, we use strong gravitational lenses as cosmological probes.
Strong lensing, as opposed to weak lensing, produces multiple images of a
single source. Extracting cosmologically relevant information requires accurate
modeling of the lens mass distribution, the latter being a galaxy or a cluster.
To this purpose a variety of models are available, but it is hard to
distinguish between them, as the choice is mostly guided by the quality of the
fit to the data without accounting for the number of additional parameters
introduced. However, this is a model selection problem rather than one of
parameter fitting that we address in the Bayesian framework. 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 data.
