Analytical and EZmock covariance validation for the DESI 2024 results

TL;DR

This paper compares analytical and EZmock covariance estimation methods for DESI 2024 cosmological analyses, finding analytical methods suitable for BAO but requiring corrections for Full Shape analyses.

Contribution

It evaluates the effectiveness of analytical and mock-based covariance matrices, recommending the former for BAO and proposing corrections for Full Shape analyses in DESI.

Findings

Analytical covariance performs well for BAO analyses.

Mock covariance is necessary for accurate Full Shape analysis.

Analytical covariance in Fourier space needs correction for Full Shape.

Abstract

The estimation of uncertainties in cosmological parameters is an important challenge in Large-Scale-Structure (LSS) analyses. For standard analyses such as Baryon Acoustic Oscillations (BAO) and Full Shape, two approaches are usually considered. First: analytical estimates of the covariance matrix use Gaussian approximations and (nonlinear) clustering measurements to estimate the matrix, which allows a relatively fast and computationally cheap way to generate matrices that adapt to an arbitrary clustering measurement. On the other hand, sample covariances are an empirical estimate of the matrix based on en ensemble of clustering measurements from fast and approximate simulations. While more computationally expensive due to the large amount of simulations and volume required, these allow us to take into account systematics that are impossible to model analytically. In this work we compare these two approaches in order to enable DESI's key analyses. We find that the configuration space analytical estimate performs satisfactorily in BAO analyses and its flexibility in terms of input clustering makes it the fiducial choice for DESI's 2024 BAO analysis. On the contrary, the analytical computation of the covariance matrix in Fourier space does not reproduce the expected measurements in terms of Full Shape analyses, which motivates the use of a corrected mock covariance for DESI's Full Shape analysis.