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Hybrid-bias and Displacement Emulators for Field-level Modelling of Galaxy Clustering in Real and Redshift Space by Ibanez, Marcos Pellejero; Angulo, Raul E.; Jamieson, Drew; Li, Yin is a scholarly article available to read on EtoBox.

What is Hybrid-bias and Displacement Emulators for Field-level Modelling of Galaxy Clustering in Real and Redshift Space about?

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.

Author

Ibanez, Marcos Pellejero; Angulo, Raul E.; Jamieson, Drew; Li, Yin

Published

2023

Language

EN