Machine-assisted semi-simulation model (MSSM): estimating galactic baryonic properties from their dark matter using a machine trained on hydrodynamic simulations

• Published in: Monthly notices of the Royal Astronomical Society Vol. 489; no. 3; pp. 3565 - 3581
• Main Authors: Jo, Yongseok, Kim, Ji-hoon
• Format: Journal Article
• Language: English
• Published: Oxford University Press 01.11.2019
• Subjects: galaxies: formation; cosmology: large-scale structure of Universe; methods: numerical; galaxies: statistics; galaxies: evolution; cosmology: dark matter
• ISSN: 0035-8711; 1365-2966
• DOI: 10.1093/mnras/stz2304

ABSTRACT 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 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.