2011/09/13 by Irène Balmès, Balmès, Irène
Computer Science · Decision Sciences · Engineering · Physics and Astronomy · #Calibration and Measurement Techniques #Cosmology and Nongalactic Astrophysics (astro-ph.CO) #FOS: Physical sciences #Gaussian Processes and Bayesian Inference #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Scientific Measurement and Uncertainty Evaluation #astro-ph.CO #astro-ph.IM
paper · pdf · doi:10.48550/arxiv.1109.2902
Proceeding of the Statistical Challenges in Modern Astronomy V conference
openalex publication_date 2011/09/13 · arxiv created 2011/12/12 · arxiv updated 2011/12/13 · openalex created_date 2022/08/29 · openalex updated_date 2026/07/28
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.