Revealing the Galaxy–Halo Connection through Machine Learning

Journal Article · 13 March 2023 · The Astrophysical Journal

DOI: https://doi.org/10.3847/1538-4357/acb25c · OSTI ID:1961683

Hausen, Ryan ; Robertson, Brant E. ; Zhu, Hanjue ; Gnedin, Nickolay Y. ; Madau, Piero ; Schneider, Evan E. ; Villasenor, Bruno ; Drakos, Nicole E.

Abstract Understanding the connections between galaxy stellar mass, star formation rate, and dark matter halo mass represents a key goal of the theory of galaxy formation. Cosmological simulations that include hydrodynamics, physical treatments of star formation, feedback from supernovae, and the radiative transfer of ionizing photons can capture the processes relevant for establishing these connections. The complexity of these physics can prove difficult to disentangle and obfuscate how mass-dependent trends in the galaxy population originate. Here, we train a machine-learning method called Explainable Boosting Machines (EBMs) to infer how the stellar mass and star formation rate of nearly 6 million galaxies simulated by the Cosmic Reionization on Computers project depend on the physical properties of halo mass, the peak circular velocity of the galaxy during its formation history v _peak , cosmic environment, and redshift. The resulting EBM models reveal the relative importance of these properties in setting galaxy stellar mass and star formation rate, with v _peak providing the most dominant contribution. Environmental properties provide substantial improvements for modeling the stellar mass and star formation rate in only ≲10% of the simulated galaxies. We also provide alternative formulations of EBM models that enable low-resolution simulations, which cannot track the interior structure of dark matter halos, to predict the stellar mass and star formation rate of galaxies computed by high-resolution simulations with detailed baryonic physics.

• Research Organization: Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
• Sponsoring Organization: National Aeronautics and Space Administration (NASA); National Science Foundation (NSF); USDOE; USDOE Office of Science (SC), High Energy Physics (HEP)
• Grant/Contract Number: AC02-06CH11357; AC02-07CH11359; AC05-00OR22725
• OSTI ID: 1961683
• Report Number(s): FERMILAB-PUB-22-370-T; arXiv:2204.10332
• Journal Information: The Astrophysical Journal, Journal Name: The Astrophysical Journal Journal Issue: 2 Vol. 945; ISSN 0004-637X
• Publisher: American Astronomical SocietyCopyright Statement
• Country of Publication: United States
• Language: English