Machine Learning and Cosmological Simulations I: Semi-Analytical Models

TL;DR

This paper introduces a machine learning framework trained on cosmological simulations to predict galaxy properties from dark matter halo features, offering a new tool for studying galaxy formation efficiently.

Contribution

It presents a novel application of machine learning to semi-analytical models, demonstrating accurate predictions of galaxy properties based on halo data.

Findings

ML models accurately predict galaxy properties at z=0

Dark matter halo features strongly influence galaxy characteristics

ML offers a computationally efficient alternative to traditional models

Abstract

We present a new exploratory framework to model galaxy formation and evolution in a hierarchical universe by using machine learning (ML). Our motivations are two-fold: (1) presenting a new, promising technique to study galaxy formation, and (2) quantitatively analyzing the extent of the influence of dark matter halo properties on galaxies in the backdrop of semi-analytical models (SAMs). We use the influential Millennium Simulation and the corresponding Munich SAM to train and test various sophisticated machine learning algorithms (k-Nearest Neighbors, decision trees, random forests and extremely randomized trees). By using only essential dark matter halo physical properties for haloes of $M>10^{12} M_{\odot}$ and a partial merger tree, our model predicts the hot gas mass, cold gas mass, bulge mass, total stellar mass, black hole mass and cooling radius at z = 0 for each central galaxy in a dark matter halo for the Millennium run. Our results provide a unique and powerful phenomenological framework to explore the galaxy-halo connection that is built upon SAMs and demonstrably place ML as a promising and a computationally efficient tool to study small-scale structure formation.