A sparse regression approach for populating dark matter halos and subhalos with galaxies

TL;DR

This paper introduces a sparse regression method to accurately predict galaxy stellar masses from dark matter halo properties, unifying central and satellite galaxies in a single model, and demonstrating its effectiveness in reproducing galaxy distribution statistics.

Contribution

The paper presents a novel application of sparse regression methods to model galaxy-halo relations, simplifying the modeling process and improving interpretability over existing machine learning approaches.

Findings

Accurately predicts galaxy stellar masses from halo properties.

Unifies modeling of central and satellite galaxies with a single model.

Reproduces galaxy stellar mass functions and correlation functions effectively.

Abstract

We use sparse regression methods (SRM) to build accurate and explainable models that predict the stellar mass of central and satellite galaxies as a function of properties of their host dark matter halos. SRM are machine learning algorithms that provide a framework for modelling the governing equations of a system from data. In contrast with other machine learning algorithms, the solutions of SRM methods are simple and depend on a relatively small set of adjustable parameters. We collect data from 35,459 galaxies from the EAGLE simulation using 19 redshift slices between $z=0$ and $z=4$ to parameterize the mass evolution of the host halos. Using an appropriate formulation of input parameters, our methodology can model satellite and central halos using a single predictive model that achieves the same accuracy as when predicted separately. This allows us to remove the somewhat arbitrary distinction between those two galaxy types and model them based only on their halo growth history. Our models can accurately reproduce the total galaxy stellar mass function and the stellar mass-dependent galaxy correlation functions ($\xi(r)$) of EAGLE. We show that our SRM model predictions of $\xi(r)$ is competitive with those from sub-halo abundance matching and might be comparable to results from extremely randomized trees. We suggest SRM as an encouraging approach for populating the halos of dark matter only simulations with galaxies and for generating mock catalogues that can be used to explore galaxy evolution or analyse forthcoming large-scale structure surveys.