Galaxy clustering from the bottom up: A Streaming Model emulator I

TL;DR

This paper introduces a neural network emulator for galaxy clustering that achieves high accuracy on simulation data, enabling improved cosmological parameter constraints, but highlights potential biases when galaxy properties are more complex than halo mass.

Contribution

The paper presents a neural network-based emulator for real-space galaxy clustering trained on N-body simulations, demonstrating high accuracy and its application in cosmological parameter inference.

Findings

Achieves sub-percent accuracy on scales 1-30 h^{-1} Mpc.

Improves sigma_8 constraints by a factor of 2 when including small scales.

Biases arise if galaxy clustering depends on properties beyond halo mass.

Abstract

In this series of papers, we present a simulation-based model for the non-linear clustering of galaxies based on separate modelling of clustering in real space and velocity statistics. In the first paper, we present an emulator for the real-space correlation function of galaxies, whereas the emulator of the real-to-redshift space mapping based on velocity statistics is presented in the second paper. Here, we show that a neural network emulator for real-space galaxy clustering trained on data extracted from the Dark Quest suite of N-body simulations achieves sub-per cent accuracies on scales $1 < r < 30 $ $h^{-1} \,\mathrm{Mpc}$, and better than $3\%$ on scales $r < 1$ $h^{-1}\mathrm{Mpc}$ in predicting the clustering of dark-matter haloes with number density $10^{-3.5}$ $(h^{-1}\mathrm{Mpc})^{-3}$, close to that of SDSS LOWZ-like galaxies. The halo emulator can be combined with a galaxy-halo connection model to predict the galaxy correlation function through the halo model. We demonstrate that we accurately recover the cosmological and galaxy-halo connection parameters when galaxy clustering depends only on the mass of the galaxies' host halos. Furthermore, the constraining power in $\sigma_8$ increases by about a factor of $2$ when including scales smaller than $5$ $h^{-1} \,\mathrm{Mpc}$. However, when mass is not the only property responsible for galaxy clustering, as observed in hydrodynamical or semi-analytic models of galaxy formation, our emulator gives biased constraints on $\sigma_8$. This bias disappears when small scales ($r < 10$ $h^{-1}\mathrm{Mpc}$) are excluded from the analysis. This shows that a vanilla halo model could introduce biases into the analysis of future datasets.