Cosmology behind the mask: Constraining the parameters of $\Lambda$CDM with the unmasked galaxy density field from VIPERS

TL;DR

This paper introduces a Gibbs sampling-based method to extract cosmological parameters from VIPERS galaxy survey data, effectively accounting for survey geometry and providing constraints consistent with traditional analyses.

Contribution

It presents a novel, fully Bayesian approach using Gibbs sampling to analyze galaxy density fields, improving parameter estimation in complex survey geometries.

Findings

Constraints on $sigma_8$ consistent with previous analyses

Method validated with mock catalogues showing robust error estimates

Approach applicable to future large-scale galaxy surveys

Abstract

Galaxy redshift surveys are designed to map cosmic structures in three dimensions for large-scale structure studies. Nevertheless, limitations due to sampling and the survey window are unavoidable and degrade the cosmological constraints. We present an analysis of the VIMOS Public Extragalactic Redshift Survey (VIPERS) over the redshift range $0.6 < z < 1$ that is optimised to extract the cosmological parameters while fully accounting for the complex survey geometry. We employ the Gibbs sampling algorithm to iteratively draw samples of the galaxy density field in redshift space, the galaxy bias, the matter density, baryon fraction and growth-rate parameter $f\sigma_8$ based on a multivariate Gaussian likelihood and prior on the density field. Despite the high number of degrees of freedom, the samples converge to the joint posterior distribution and give self-consistent constraints on the model parameters. We validate the approach using VIPERS mock galaxy catalogues. Although the uncertainty is underestimated by the Gaussian likelihood on the scales that we consider by 50 %, the dispersion of the results from the mock catalogues gives a robust error estimate. We find that the precision of the results matches those of the traditional analyses applied to the VIPERS data that use more constrained models. By relaxing the model assumptions, we confirm that the data deliver consistent constraints on the $\Lambda$CDM model. This work provides a case-study for the application of maximum-likelihood analyses for the next generation of galaxy redshift surveys.