Bayesian analysis of spatially distorted cosmic signals from Poissonian data

Abstract

Reconstructing the matter density field from galaxy counts is a problem frequently addressed in current literature. Two main sources of error are shot noise from galaxy counts and insufficient knowledge of the correct galaxy position caused by peculiar velocities and redshift measurement uncertainty. Here we address the reconstruction problem of a Poissonian sampled log-normal density field with velocity distortions in a Bayesian way via a maximum a posteriory method. We test our algorithm on a 1D toy case and find significant improvement compared to simple data inversion. In particular, we address the following problems: photometric redshifts, mapping of extended sources in coded mask systems, real space reconstruction from redshift space galaxy distribution and combined analysis of data with different point spread functions.

19 pages, 10 figures, accepted

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Cited by in corpus (3)

• NIFTY - Numerical Information Field Theory - a versatile Python library for signal inference
• Rejuvenating Power Spectra II: the Gaussianized galaxy density field
• Bayesian inference from photometric redshift surveys