A modern halo streaming model for redshift space distortions

TL;DR

This paper introduces a physically interpretable halo streaming model for nonlinear redshift-space galaxy clustering, combining emulators trained on simulations to accurately and efficiently support cosmological analyses.

Contribution

It presents a novel halo streaming model that integrates emulators of key physical ingredients, enhancing accuracy, interpretability, and flexibility for RSD analyses in large-scale surveys.

Findings

Reproduces nonlinear anisotropic clustering down to small scales

Achieves computational efficiency suitable for cosmological inference

Provides publicly available codes and emulators

Abstract

Accurate modelling of redshift-space distortions (RSD) in galaxy clustering is essential for extracting cosmological information from current and forthcoming large-scale structure surveys. While perturbation theory is reliable on large scales, much of the constraining power lies at intermediate and small separations, where nonlinear dynamics within and between dark matter haloes dominate. We present a halo streaming model for nonlinear galaxy clustering in redshift space that is accurate and physically interpretable. Our framework combines the streaming model for RSD with a halo-model decomposition of the galaxy clustering into central/satellite and one-/two-halo contributions. We build dedicated emulators for the key physical ingredients, trained on a suite of $N$-body simulations: halo mass functions, real-space halo two-point correlation functions, and pairwise velocity moments. By emulating these modular building blocks rather than the final redshift-space observable, this approach preserves physical transparency, enables targeted optimisation for each ingredient, and remains flexible to changes in tracer populations and galaxy-halo connection models. The resulting halo streaming model reproduces the simulated nonlinear anisotropic clustering signal down to highly nonlinear scales, while achieving the computational efficiency required for cosmological parameter inference. This framework is designed to support full-shape RSD analyses for surveys such as DESI and \textit{Euclid}, facilitating precision measurements of structure growth and tests of gravity. All codes and trained emulators are publicly available in the \href{https://github.com/chzruan/freyja}{\texttt{freyja}} repository.