Exploring HOD-dependent systematics for the DESI 2024 Full-Shape galaxy clustering analysis

TL;DR

This paper investigates how uncertainties in the galaxy-halo connection model affect cosmological parameter inference from DESI galaxy clustering data, proposing a systematic covariance approach to improve robustness.

Contribution

It introduces a new method to incorporate systematic uncertainties from HOD modeling into the covariance matrix for more reliable cosmological inference.

Findings

HOD model uncertainties can cause shifts >20% of statistical errors.

The proposed covariance method provides conservative yet robust uncertainty estimates.

The approach improves the reliability of DESI cosmological results.

Abstract

We analyse the robustness of the DESI 2024 cosmological inference from the full shape of the galaxy power spectrum to uncertainties in the Halo Occupation Distribution (HOD) model of the galaxy-halo connection and the choice of priors on nuisance parameters. We assess variations in the recovered cosmological parameters across a range of mocks populated with different HOD models and find that shifts are often greater than 20% of the expected statistical uncertainties from the DESI data. We encapsulate the effect of such shifts in terms of a systematic covariance term, $\mathsf{C}_{\rm HOD}$, and an additional diagonal contribution quantifying the impact of our choice of nuisance parameter priors on the ability of the effective field theory (EFT) model to correctly recover the cosmological parameters of the simulations. These two covariance contributions are designed to be added to the usual covariance term, $\mathsf{C}_{\rm stat}$, describing the statistical uncertainty in the power spectrum measurement, in order to fairly represent these sources of systematic uncertainty. This novel approach should be more general and robust to the choice of model or additional external datasets used in cosmological fits than the alternative approach of adding systematic uncertainties to the recovered marginalised parameter posteriors. We compare the approaches within the context of a fixed $\Lambda$CDM model and demonstrate that our method gives conservative estimates of the systematic uncertainty that nevertheless have little impact on the final posteriors obtained from DESI data.