Kavli Affiliate: Nickolay Y. Gnedin
| First 5 Authors: Ryan Hausen, Brant E. Robertson, Hanjue Zhu, Nickolay Y. Gnedin, Piero Madau
| Summary:
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.
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