Populating Galaxies Into Halos Via Machine Learning on the Simba Simulation

Pratyush Kumar Das; Romeel Davé; Weiguang Cui

doi:10.1093/mnras/staf2096

paper

Populating Galaxies Into Halos Via Machine Learning on the Simba Simulation

arXiv:2406.16103 · doi:10.1093/mnras/staf2096

Abstract

We present a machine-learning framework, Machine Inferred Galaxy (MIG), to populate dark-matter haloes with galaxies in N-body simulations. MIG predicts stellar mass ( $M_{*}$ ), star-formation rate (SFR), atomic and molecular gas masses ( $M_{HI}$ and $M_{H_{2}}$ ), and metallicity, and can be extended to other properties and simulations. The pipeline first separates haloes into centrals and satellites, then uses classifiers to distinguish star-forming (SF) from quenched (Q) systems, followed by regressors trained on the SF subsets for both centrals and satellites. Trained on the $(100, h^{- 1}, Mpc)^{3}$ SIMBA galaxy-formation simulation at $z = 0$ , MIG achieves high accuracy for key baryonic properties (e.g. $R^{2} \approx 0.9$ for $M_{HI}$ of central galaxies), and remains robust at $z = 1$ and $z = 2$ . Training on fractional quantities (e.g. $M_{HI} / M_{*}$ ) and rescaling by predicted $M_{*}$ improves performance over direct predictions across properties and redshifts. MIG also reproduces galaxy mass distribution functions with higher fidelity, enabling accurate predictions of integrated tracers such as H I intensity maps. MIG therefore provides an efficient, physically consistent route to generate mock galaxy catalogues and baryonic tracers in large cosmological volumes for upcoming surveys.

14 pages, 9 figures. Accepted for publication in MNRAS

Populating Galaxies Into Halos Via Machine Learning on the Simba Simulation

Abstract

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