A flexible subhalo abundance matching model for galaxy clustering in redshift space

TL;DR

This paper introduces an advanced subhalo abundance matching model that accurately reproduces galaxy clustering in both real and redshift space using low-resolution simulations, incorporating galaxy assembly bias and star formation recipes.

Contribution

The authors develop a flexible SHAM extension that includes orphan and tidal disruption prescriptions, galaxy assembly bias, and SFR modeling, validated against hydrodynamical simulations and applied to SDSS data.

Findings

Accurately reproduces galaxy clustering and higher-order statistics.

Retrieves correct halo occupation distribution and galaxy assembly bias levels.

Fits SDSS galaxy clustering with similar accuracy to hydrodynamical simulations.

Abstract

We develop an extension of subhalo abundance matching (SHAM) capable of accurately reproducing the real and redshift-space clustering of galaxies in a state-of-the-art hydrodynamical simulation. Our method uses a low-resolution gravity-only simulation and it includes orphan and tidal disruption prescriptions for satellite galaxies, and a flexible amount of galaxy assembly bias. Furthermore, it includes recipes for star formation rate (SFR) based on the dark matter accretion rate. We test the accuracy of our model against catalogues of stellar-mass- and SFR-selected galaxies in the TNG300 hydrodynamic simulation. By fitting a small number of free parameters, our extended SHAM reproduces the projected correlation function and redshift-space multipoles for number densities $10^{-3} - 10^{-2}\, h^{3}{\rm Mpc}^{-3}$, at $z=1$ and $z=0$, and for scales $r \in [0.3 - 20] h^{-1}{\rm Mpc}$. Simultaneously, the SHAM results also retrieve the correct halo occupation distribution, the level of galaxy assembly bias, and higher-order statistics present in the TNG300 galaxy catalogues. As an application, we show that our model simultaneously fits the projected correlation function of the SDSS in 3 disjoint stellar mass bins, with an accuracy similar to that of TNG300 galaxies. This SHAM extension can be used to get accurate clustering prediction even when using low and moderate-resolution simulations.