The impact of systematic uncertainties in N-body simulations on the precision cosmology from galaxy clustering: a halo model approach

TL;DR

This paper evaluates how uncertainties in N-body simulations, especially velocity bias, affect the precision of cosmological parameters derived from galaxy clustering data, highlighting calibration as a key challenge.

Contribution

It introduces a halo model approach to quantify the impact of simulation uncertainties on cosmological inference, emphasizing velocity bias as the main systematic concern.

Findings

Velocity bias is the dominant source of systematic bias.

Current uncertainties limit the maximum usable scale to 0.14 h/Mpc.

Density profile and occupation statistics uncertainties are less significant.

Abstract

Dark matter N-body simulations provide a powerful tool to model the clustering of galaxies and help interpret the results of galaxy redshift surveys. However, the galaxy properties predicted from N-body simulations are not necessarily representative of the observed galaxy populations; for example, theoretical uncertainties arise from the absence of baryons in N-body simulations. In this work, we assess how the uncertainties in N-body simulations impact the cosmological parameters inferred from galaxy redshift surveys. Applying the halo model framework, we find that the velocity bias of galaxies in modeling the redshift-space distortions is likely to be the predominant source of systematic bias. For a deep, wide survey like BigBOSS, current 10 per cent uncertainties in the velocity bias limit k_max to 0.14 h/Mpc. In contrast, we find that the uncertainties related to the density profiles and the galaxy occupation statistics lead to relatively insignificant systematic biases. Therefore, the ability to calibrate the velocity bias accurately -- from observations as well as simulations -- will likely set the ultimate limit on the smallest length scale that can be used to infer cosmological information from galaxy clustering.