Hybrid-bias and displacement emulators for field-level modelling of galaxy clustering in real and redshift space

TL;DR

This paper demonstrates that combining hybrid bias expansions with field-level emulators enables highly accurate modeling of galaxy clustering in real and redshift space, achieving 1-2% precision up to certain scales.

Contribution

It introduces a novel combined approach of hybrid bias expansions and field-level emulators for galaxy clustering modeling, showing high accuracy in both real and redshift space.

Findings

Emulators accurately predict all operators of second-order hybrid bias expansion.

Achieves 1-2% precision in power spectrum for BOSS and Euclid-like samples up to k~0.6 h/Mpc.

Provides precise field-level galaxy statistics predictions.

Abstract

Recently, hybrid bias expansions have emerged as a powerful approach to modelling the way in which galaxies are distributed in the Universe. Similarly, field-level emulators have recently become possible thanks to advances in machine learning and $N$-body simulations. In this paper we explore whether both techniques can be combined to provide a field-level model for the clustering of galaxies in real and redshift space. Specifically, here we will demonstrate that field-level emulators are able to accurately predict all the operators of a $2^{\rm nd}$-order hybrid bias expansion. The precision achieved in real and redshift space is similar to that obtained for the nonlinear matter power spectrum. This translates to roughly 1-2\% precision for the power spectrum of a BOSS and a Euclid-like galaxy sample up to $k\sim 0.6 h^{-1}$Mpc. Remarkably, this combined approach also delivers precise predictions for field-level galaxy statistics. Despite all these promising results, we detect several areas where further improvements are required. Therefore, this work serves as a road-map for the developments required for a more complete exploitation of upcoming large-scale structure surveys.