Populating Galaxies Into Halos Via Machine Learning on the Simba Simulation

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 ($M_\ast$), star-formation rate (SFR), atomic and molecular gas masses ($M_{\mathrm{HI}}$ and $M_{\mathrm{H_2}}$), 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 $(100,h^{-1},\mathrm{Mpc})^3$ SIMBA galaxy-formation simulation at $z=0$, MIG achieves high accuracy for key baryonic properties (e.g. $R^2 \approx 0.9$ for $M_{\mathrm{HI}}$ of central galaxies), and remains robust at $z=1$ and $z=2$. Training on fractional quantities (e.g. $M_{\mathrm{HI}}/M_\ast$) and rescaling by predicted $M_\ast$ 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.