SUNBIRD: A simulation-based model for full-shape density-split clustering

TL;DR

This paper introduces a neural network emulator-based model for density-split clustering that accurately captures cosmological dependencies down to small scales, improving parameter constraints when combined with traditional galaxy clustering analyses.

Contribution

The authors develop a simulation-based neural network model for full-shape density-split clustering that achieves high accuracy and enhances cosmological parameter constraints beyond standard methods.

Findings

Model reaches sub-percent accuracy down to 1 h^{-1} Mpc

Combining DSC with 2PCF tightens constraints on key parameters by factors of 2-3

Model is robust against different galaxy-halo connection assumptions

Abstract

Combining galaxy clustering information from regions of different environmental densities can help break cosmological parameter degeneracies and access non-Gaussian information from the density field that is not readily captured by the standard two-point correlation function (2PCF) analyses. However, modelling these density-dependent statistics down to the non-linear regime has so far remained challenging. We present a simulation-based model that is able to capture the cosmological dependence of the full shape of the density-split clustering (DSC) statistics down to intra-halo scales. Our models are based on neural-network emulators that are trained on high-fidelity mock galaxy catalogues within an extended-$\Lambda$CDM framework, incorporating the effects of redshift-space, Alcock-Paczynski distortions and models of the halo-galaxy connection. Our models reach sub-percent level accuracy down to $1\,h^{-1}{\rm Mpc}$ and are robust against different choices of galaxy-halo connection modelling. When combined with the galaxy 2PCF, DSC can tighten the constraints on $\omega_{\rm cdm}$, $\sigma_8$, and $n_s$ by factors of 2.9, 1.9, and 2.1, respectively, compared to a 2PCF-only analysis. DSC additionally puts strong constraints on environment-based assembly bias parameters. Our code is made publicly available on Github.