Machine-assisted Semi-Simulation Model (MSSM): Estimating Galactic Baryonic Properties from their Dark Matter using a Machine Trained on Hydrodynamic Simulations

TL;DR

This paper introduces MSSM, a machine learning pipeline trained on hydrodynamic simulations to accurately predict baryonic galaxy properties from dark matter data in large-scale DM-only simulations, enabling efficient galaxy catalog generation.

Contribution

The paper presents a novel machine learning method with improved accuracy for estimating baryonic properties from dark matter halos, bridging hydrodynamic and DM-only simulations.

Findings

Enhanced accuracy in predicting stellar mass and star formation rate.

Validation of the pipeline on large DM-only simulations.

Compatibility of MSSM with semi-analytic models.

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 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.