Iterative removal of redshift space distortions from galaxy clustering

TL;DR

This paper presents an iterative nonlinear reconstruction algorithm that effectively removes redshift-space distortions from galaxy clustering data, improving the accuracy of real-space galaxy correlation functions and aiding cosmological parameter estimation.

Contribution

The authors introduce a novel iterative nonlinear reconstruction method to remove RSD effects, enhancing the recovery of real-space galaxy clustering and initial density fields.

Findings

Achieves ~1% accuracy in real-space galaxy correlation functions

Restores quadrupole to zero on scales s ≥ 20 Mpc/h

Improves reconstruction of initial density fields

Abstract

Observations of galaxy clustering are made in redshift space, which results in distortions to the underlying isotropic distribution of galaxies. These redshift-space distortions (RSD) not only degrade important features of the matter density field, such as the baryonic acoustic oscillation (BAO) peaks, but also pose challenges for the theoretical modelling of observational probes. Here we introduce an iterative nonlinear reconstruction algorithm to remove RSD effects from galaxy clustering measurements, and assess its performance by using mock galaxy catalogues. The new method is found to be able to recover the real-space galaxy correlation function with an accuracy of $\sim1\%$, and restore the quadrupole accurately to $0$, on scales $s\gtrsim20\Mpch$. It also leads to an improvement in the reconstruction of the initial density field, which could help to accurately locate the BAO peaks. An `internal calibration' scheme is proposed to determine the values of cosmological parameters as a part of the reconstruction process, and possibilities to break parameter degeneracies are discussed. RSD reconstruction can offer a potential way to simultaneously extract the cosmological parameters, initial density field, real-space galaxy positions and large-scale peculiar velocity field (of the real Universe), making it an alternative to standard perturbative approaches in galaxy clustering analysis, bypassing the need for RSD modelling.