High-Precision Galaxy Clustering Predictions from Small-Volume Hydrodynamical Simulations via Control Variates

TL;DR

This paper introduces a method using control variates to generate high-precision galaxy clustering predictions from small-volume hydrodynamical simulations, significantly reducing computational costs for large-scale structure analysis.

Contribution

The authors develop a novel control variate technique that combines small-volume hydrodynamical and gravity-only simulations to produce accurate galaxy clustering predictions at large volumes.

Findings

Effective volume increased up to 100 times for clustering predictions.

Method achieves high precision suitable for next-generation surveys.

Reduces computational costs compared to full hydrodynamical simulations.

Abstract

Cosmological simulations of galaxy formation are an invaluable tool for understanding galaxy formation and its impact on cosmological parameter inference from large-scale structure. However, their high computational cost is a significant obstacle for running simulations that probe cosmological volumes comparable to those analyzed by contemporary large-scale structure experiments. In this work, we explore the possibility of obtaining high-precision galaxy clustering predictions from small-volume hydrodynamical simulations such as MilleniumTNG and FLAMINGO via control variates. In this approach, the hydrodynamical full-physics simulation is paired with a matched low-resolution gravity-only simulation. By learning the galaxy-halo connection from the hydrodynamical simulation and applying it to the gravity-only counterpart, one obtains a galaxy population that closely mimics the one in the more expensive simulation. One can then construct an estimator of galaxy clustering that combines the clustering amplitudes in the small-volume hydrodynamical and gravity-only simulations with clustering amplitudes in a large-volume gravity-only simulation. Depending on the galaxy sample, clustering statistic, and scale, this galaxy clustering estimator can have an effective volume of up to around $100$ times the volume of the original hydrodynamical simulation in the non-linear regime. With this approach, we can construct galaxy clustering predictions from existing simulations that are precise enough for mock analyses of next-generation large-scale structure surveys such as the Dark Energy Spectroscopic Instrument and the Legacy Survey of Space and Time.