MG14 - Talk detail

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Abstract

To accomplish correct Bayesian inference from weak lensing shear data requires a complete statistical description of the data. The natural framework to do this is a Bayesian Hierarchical Model, which divides the chain of reasoning into its component steps. Starting with a catalogue of shear estimates in tomographic bins, we build a model that allows us to sample simultaneously from the the underlying tomographic shear fields and the relevant power spectra (E-mode, B-mode, and E-B, for auto- and cross-power spectra). The procedure deals seamlessly with masked data and intrinsic alignments. Using Gibbs sampling and messenger fields, we show with simulated data that the large-dimensional parameter space (over 67000 dimensions in this case) can be efficiently sampled and the full joint posterior probability density function for the parameters can be obtained in a computationally tractable way. The method correctly recovers the underlying shear fields and all of the power spectra, including the very small B-mode present in the simulations, even at a level two orders of magnitude below the shot noise, and 3 below the E mode. Samples of the PDF can then be used to infer cosmological parameters.

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