TL;DR
This paper introduces a novel approach to model redshift-space distortions by splitting galaxy density fields based on local density, using cross-correlation functions to improve cosmological parameter constraints in the non-linear regime.
Contribution
The study demonstrates that combining cross-correlation functions of split densities enhances RSD modeling accuracy and cosmological constraints compared to traditional two-point correlation functions.
Findings
Gaussian streaming model performs well with split densities
Improved constraints on $f\sigma_{12}$ and AP parameters by factors of 6 and 30
BAO features in all CCFs enable independent AP parameter constraints
Abstract
Accurate modelling of redshift-space distortions (RSD) is challenging in the non-linear regime for two-point statistics e.g. the two-point correlation function (2PCF). We take a different perspective to split the galaxy density field according to the local density, and cross-correlate those densities with the entire galaxy field. Using mock galaxies, we demonstrate that combining a series of cross-correlation functions (CCFs) offers improvements over the 2PCF as follows: 1. The distribution of peculiar velocities in each split density is nearly Gaussian. This allows the Gaussian streaming model for RSD to perform accurately within the statistical errors of a (Gpc) volume for almost all scales and all split densities. 2. The PDF of the density field at small scales is non-Gaussian, but the CCFs of split densities capture the non-Gaussianity, leading to improved…
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