Growth rate measurements from a joint analysis of the large-scale galaxy clustering in Fourier and configuration space

TL;DR

This paper introduces a framework for analyzing redshift-space distortions in galaxy clustering data using both Fourier and configuration space, demonstrating unbiased results with simulations and real data.

Contribution

It presents a novel joint analysis method in Fourier and configuration space for galaxy clustering, improving robustness and consistency of cosmological constraints.

Findings

Joint space inference yields unbiased constraints.

Results are consistent with previous methods.

Applied to BOSS+eBOSS data, obtaining $f\sigma_8$ in agreement with official results.

Abstract

In this work, we test a framework to perform the analysis of redshift-space distortions simultaneously in configuration and Fourier space. We test our methods with the AbacusSummit suite of N-body simulations as well as a more numerous set of approximate EZmocks, reproducing the sample of luminous red galaxies of from the Baryon Oscillation Spectroscopic Survey (BOSS) and its extension (eBOSS). Our clustering models are based on the effective field theory of large-scale structures in a Lagrangian frame, used in the latest results from the Dark Energy Spectroscopic Instrument. We perform a template type of analysis, including dilation parameters and the slope parameter from the ShapeFit framework. We find that the joint space inference yields unbiased and robust constraints on simulated datasets, consistent with results from individual spaces or previous methods to obtain consensus results. Our joint space analysis on the the BOSS+eBOSS LRG sample obtains $ f\sigma_8 = 0.463 \pm 0.052 $, in good agreement with the official 2020 results.