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 darkmatter (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) 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) 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 semisimulation 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 on to 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.