Bayesian redshift-space distortions correction from galaxy redshift surveys
Francisco-Shu Kitaura, Metin Ata, Raul E. Angulo, Chia-Hsun Chuang,, Sergio Rodriguez-Torres, Carlos Hernandez Monteagudo, Francisco Prada, and, Gustavo Yepes

TL;DR
This paper introduces a Bayesian method to reconstruct real-space galaxy positions from redshift-space data, correcting for distortions and biases to improve cosmological measurements.
Contribution
The paper presents a novel Bayesian reconstruction technique that models stochastic bias and redshift distortions, enabling more accurate galaxy distance estimations from redshift surveys.
Findings
Potential to constrain growth rate up to k ~ 0.3 h/Mpc
Can correct for photo-metric redshift errors
Improves BAO reconstruction accuracy
Abstract
We present a Bayesian reconstruction method which maps a galaxy distribution from redshift-space 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 given by the negative binomial distribution function modelling stochastic bias. We assume a deterministic bias given by a power law relating the dark matter density field to the expected halo or galaxy field. Coherent redshift-space distortions are corrected in a Gibbs-sampling procedure by moving the galaxies from redshift-space to real-space according to the peculiar motions derived from the recovered density field using linear theory with the option to include tidal field corrections from second order Lagrangian perturbation theory. The virialised distortions are corrected by sampling candidate real-space positions (being in the…
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