Modelling galaxy clustering in redshift space with a Lagrangian bias formalism and $N$-body simulations

TL;DR

This paper introduces a new galaxy clustering model in redshift space that combines second-order Lagrangian bias expansion with N-body simulation data, accurately capturing small-scale distortions to enhance cosmological analyses.

Contribution

The authors develop and validate a novel model that accurately describes galaxy clustering and redshift-space distortions down to small scales using a second-order Lagrangian bias formalism and simulation-based velocity fields.

Findings

Model accurately describes monopole, quadrupole, and hexadecapole of galaxy power spectra.

Effective down to scales of approximately 0.6 h/Mpc.

Potential to improve cosmological parameter constraints from future surveys.

Abstract

Improving the theoretical description of galaxy clustering on small scales is an important challenge in cosmology, as it can considerably increase the scientific return of forthcoming galaxy surveys -- e.g. tightening the bounds on neutrino masses and deviations from general relativity. In this paper, we propose and test a new model for the clustering of galaxies that is able to accurately describe redshift-space distortions even down to small scales. This model corresponds to a second-order perturbative Lagrangian bias expansion which is advected to Eulerian space employing a displacement field extracted from $N$-body simulations. Eulerian coordinates are then transformed into redshift space by directly employing simulated velocity fields augmented with nuisance parameters capturing various possible satellite fractions and intra-halo small-scale velocities. We quantify the accuracy of our approach against samples of physically-motivated mock galaxies selected according to either Stellar Mass (SM) or Star Formation Rate (SFR) at multiple abundances and at $z=0$ and $1$. We find our model describes the monopole, quadrupole, and hexadecapole of the galaxy-power spectra down to scales of $k\approx 0.6 [h/$Mpc] within the accuracy of our simulations. This approach could pave the way to significantly increase the amount of cosmological information to be extracted from future galaxy surveys.