The DESI $N$-body Simulation Project -- II. Suppressing sample variance with fast simulations

TL;DR

This paper introduces a method combining high-resolution simulations with fast approximate solvers to significantly reduce sample variance, enabling more precise cosmological analyses with fewer simulations.

Contribution

It proposes a novel variance suppression technique using paired simulations and fast solvers, extending accurate analysis to multiple cosmologies with minimal computational resources.

Findings

Achieves 100 times smaller variance than DESI statistical errors at relevant scales.

Effectively suppresses sample variance of the halo bispectrum.

Extends accurate simulation coverage to multiple cosmologies with only one realization per cosmology.

Abstract

Dark Energy Spectroscopic Instrument (DESI) will construct a large and precise three-dimensional map of our Universe. The survey effective volume reaches $\sim20\Gpchcube$. It is a great challenge to prepare high-resolution simulations with a much larger volume for validating the DESI analysis pipelines. \textsc{AbacusSummit} is a suite of high-resolution dark-matter-only simulations designed for this purpose, with $200\Gpchcube$ (10 times DESI volume) for the base cosmology. However, further efforts need to be done to provide a more precise analysis of the data and to cover also other cosmologies. Recently, the CARPool method was proposed to use paired accurate and approximate simulations to achieve high statistical precision with a limited number of high-resolution simulations. Relying on this technique, we propose to use fast quasi-$N$-body solvers combined with accurate simulations to produce accurate summary statistics. This enables us to obtain 100 times smaller variance than the expected DESI statistical variance at the scales we are interested in, e.g. $k < 0.3\hMpc$ for the halo power spectrum. In addition, it can significantly suppress the sample variance of the halo bispectrum. We further generalize the method for other cosmologies with only one realization in \textsc{AbacusSummit} suite to extend the effective volume $\sim 20$ times. In summary, our proposed strategy of combining high-fidelity simulations with fast approximate gravity solvers and a series of variance suppression techniques sets the path for a robust cosmological analysis of galaxy survey data.