A sparse regression approach for populating dark matter haloes and subhaloes with galaxies

Monthly Notices of the Royal Astronomical Society, vol. 518, pp. 2903–2920

Abstract

ABSTRACT We use sparse regression methods (SRMs) 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 haloes. SRMs 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 parametrize the mass evolution of the host haloes. Using an appropriate formulation of input parameters, our methodology can model satellite and central haloes 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 (ξ(r)) of EAGLE. We show that our SRM model predictions of ξ(r) is competitive with those from subhalo abundance matching and might be comparable to results from extremely randomized trees. We suggest SRM as an encouraging approach for populating the haloes 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.

Authors 5

1. Miguel Icaza-Lizaola corresponding

   Durham University · Korea Astronomy and Space Science Institute

   Affiliation as printed

   Institute for Computational Cosmology, Department of Physics, Durham University , South Road, Durham DH1 3LE, UK

   Institute for Data Science, Department of Physics, Durham University , South Road, Durham DH1 3LE, UK

   Korea Astronomy and Space Science Institute , 776 Daedeok-daero, Yuseong-gu, Daejeon 34055, Republic of Korea

2. Richard G. Bower

   Durham University

   Affiliation as printed

   Centre for Extragalactic Astronomy, Department of Physics, Durham University , South Road, Durham DH1 3LE, UK

   Institute for Computational Cosmology, Department of Physics, Durham University , South Road, Durham DH1 3LE, UK

   Institute for Data Science, Department of Physics, Durham University , South Road, Durham DH1 3LE, UK

3. Peder Norberg

   Durham University

   Affiliation as printed

   Centre for Extragalactic Astronomy, Department of Physics, Durham University , South Road, Durham DH1 3LE, UK

   Institute for Computational Cosmology, Department of Physics, Durham University , South Road, Durham DH1 3LE, UK

   Institute for Data Science, Department of Physics, Durham University , South Road, Durham DH1 3LE, UK

4. S. Cole

   Durham University

   Affiliation as printed

   Institute for Computational Cosmology, Department of Physics, Durham University , South Road, Durham DH1 3LE, UK

5. Matthieu Schaller Aachen Leiden Observatory Lorentz Institute for Theoretical Physics

   Leiden University · Leiden Observatory

   Affiliation as printed

   Leiden Observatory, Leiden University , PO Box 9513, NL-2300 RA Leiden, the Netherlands

   Lorentz Institute for Theoretical Physics, Leiden University , PO Box 9506, NL-2300 RA Leiden, the Netherlands