Machine-assisted Semi-Simulation Model (MSSM): Estimating Galactic
  Baryonic Properties from their Dark Matter using a Machine Trained on
  Hydrodynamic Simulations
  We present a pipeline to estimate baryonic properties of a galaxy inside a
dark matter (DM) halo in DM-only simulations using a machine trained on
high-resolution hydrodynamic simulations. As an example, we use the
IllustrisTNG hydrodynamic simulation of a $(75 \,\,h^{-1}{\rm Mpc})^3$ volume
to train our machine to predict e.g., stellar mass and star formation rate in a
galaxy-sized halo based purely on its DM content. An extremely randomized tree
(ERT) algorithm is used together with multiple novel improvements we introduce
here such as a refined error function in machine training and two-stage
learning. Aided by these improvements, our model demonstrates a significantly
increased accuracy in predicting baryonic properties compared to prior attempts
--- in other words, the machine better mimics IllustrisTNG's galaxy-halo
correlation. By applying our machine to the MultiDark-Planck DM-only simulation
of a large $(1 \,\,h^{-1}{\rm Gpc})^3$ volume, we then validate the pipeline
that rapidly generates a galaxy catalogue from a DM halo catalogue using the
correlations the machine found in IllustrisTNG. We also compare our galaxy
catalogue with the ones produced by popular semi-analytic models (SAMs). Our
so-called machine-assisted semi-simulation model (MSSM) is shown to be largely
compatible with SAMs, and may become a promising method to transplant the
baryon physics of galaxy-scale hydrodynamic calculations onto a larger-volume
DM-only run. We discuss the benefits that machine-based approaches like this
entail, as well as suggestions to raise the scientific potential of such
approaches.
