Bayesian redshift-space distortions correction from galaxy redshift surveys

Abstract

AbstractWe present a Bayesian reconstruction method which maps a galaxy distribution from redshift- to real-space inferring the distances of the individual galaxies. The method is based on sampling density fields assuming a lognormal prior with a likelihood modelling non-linear stochastic bias. Coherent redshift-space distortions are corrected in a Gibbs-sampling procedure by moving the galaxies from redshift- to real-space according to the peculiar motions derived from the recovered density field using linear theory. The virialized distortions are corrected by sampling candidate real-space positions along the line of sight, which are compatible with the bulk flow corrected redshift-space position adding a random dispersion term in high-density collapsed regions (defined by the eigenvalues of the Hessian). This approach presents an alternative method to estimate the distances to galaxies using the three-dimensional spatial information, and assuming isotropy. Hence the number of applications is very broad. In this work, we show the potential of this method to constrain the growth rate up to k ∼ 0.3 h Mpc−1. Furthermore it could be useful to correct for photometric redshift errors, and to obtain improved baryon acoustic oscillations (BAO) reconstructions.