Irène Balmès, Pier-Stefano Corasaniti
Bayesian model selection methods provide a self-consistent probabilistic framework to test the validity of competing scenarios given a set of data. We present a case study application to strong gravitational lens parametric models. To this end we confront a lens power-law potential against the presence of external shear on a sample of double-image quasars using measurements of the image positions and time-delays. Our goal is to select a homogeneous lens subsample suitable for cosmological parameter inference. In the case of B1600+434, SBS 1520+530 and SDSS J1650+4251 the Bayes' factor analysis favors a simple power-law model description with high statistical significance. The combined likelihood data analysis of such systems gives the Hubble constant H_0 = 72^{+22}_{-40} km s^{-1}Mpc^{-1} for a flat \LambdaCDM cosmology having marginalized over the lens model parameters, the cosmic matter density and consistently propagated the observational errors on the angular position of the images. The next generation of cosmic structure surveys will provide larger lens datasets and the method described here can be particularly useful to select homogeneous lens subsamples adapt to perform unbiased cosmological parameter inference.
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http://arxiv.org/abs/1206.5801
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