A sparse regression approach to modelling the relation between galaxy stellar masses and their host haloes
Abstract
Sparse regression algorithms have been proposed as the appropriate framework to model the governing equations of a system from data, without needing prior knowledge of the underlying physics. In this work, we use sparse regression to build an accurate and explainable model of the stellar mass of central galaxies given properties of their host dark matter (DM) halo. Our data set comprises 9521 central galaxies from the EAGLE hydrodynamic simulation. By matching the host haloes to a DMonly simulation, we collect the halo mass and specific angular momentum at present time and for their main progenitors in 10 redshift bins from z = 0 to z = 4. The principal component of our governing equation is a thirdorder polynomial of the host halo mass, which models the stellarmasshalomass relation. The scatter about this relation is driven by the halo mass evolution and is captured by second and thirdorder correlations of the halo mass evolution with the present halo mass. An advantage of sparse regression approaches is that unnecessary terms are removed. Although we include information on halo specific angular momentum, these parameters are discarded by our methodology. This suggests that halo angular momentum has little connection to galaxy formation efficiency. Our model has a root mean square error (RMSE) of 0.167log_{10}(M*/M_{⊙}), and accurately reproduces both the stellar mass function and central galaxy correlation function of EAGLE. The methodology appears to be an encouraging approach for populating the haloes of DMonly simulations with galaxies, and we discuss the next steps that are required.
 Publication:

Monthly Notices of the Royal Astronomical Society
 Pub Date:
 November 2021
 DOI:
 10.1093/mnras/stab2368
 arXiv:
 arXiv:2101.02986
 Bibcode:
 2021MNRAS.507.4584I
 Keywords:

 galaxies: evolution;
 galaxies: haloes;
 dark matter;
 Astrophysics  Astrophysics of Galaxies
 EPrint:
 20 pages, 15 figures