Revealing the Galaxy-Halo Connection Through Machine Learning

TL;DR

This paper uses machine learning to uncover how galaxy properties like stellar mass and star formation rate depend on dark matter halo characteristics, highlighting the dominant role of peak circular velocity and the limited impact of environment for most galaxies.

Contribution

It introduces Explainable Boosting Machines to interpret galaxy formation dependencies in simulations, providing insights and models applicable to low-resolution simulations.

Findings

Peak circular velocity ($v_ ext{peak}$) is the most influential property.

Environmental factors significantly affect only a small subset of galaxies.

EBM models can predict galaxy properties across different simulation resolutions.

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 (CROC) project depend on the physical properties of halo mass, the peak circular velocity of the galaxy during its formation history $v_\mathrm{peak}$, 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 $v_\mathrm{peak}$ providing the most dominant contribution. Environmental properties provide substantial improvements for modeling the stellar mass and star formation rate in only $\lesssim10\%$ 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.