A machine learning approach to mapping baryons onto dark matter haloes using the EAGLE and C-EAGLE simulations

TL;DR

This paper introduces a machine learning method trained on high-resolution simulations to predict baryonic properties of galaxies from dark matter haloes, enabling large-volume predictions with reduced computational costs.

Contribution

The authors develop a novel machine learning framework that maps dark matter halo properties to baryonic galaxy features, extending predictions to larger cosmological volumes beyond direct hydrodynamic simulations.

Findings

Successfully reproduces key distribution functions of baryons.

Learns environmental bias in galaxy evolution.

Enables large-volume baryonic predictions from dark matter only simulations.

Abstract

High-resolution cosmological hydrodynamic simulations are currently limited to relatively small volumes due to their computational expense. However, much larger volumes are required to probe rare, overdense environments, and measure clustering statistics of the large scale structure. Typically, zoom simulations of individual regions are used to study rare environments, and semi-analytic models and halo occupation models applied to dark matter only (DMO) simulations are used to study the Universe in the large-volume regime. We propose a new approach, using a machine learning framework to explore the halo-galaxy relationship in the periodic EAGLE simulations, and zoom C-EAGLE simulations of galaxy clusters. We train a tree based machine learning method to predict the baryonic properties of galaxies based on their host dark matter halo properties. The trained model successfully reproduces a number of key distribution functions for an infinitesimal fraction of the computational cost of a full hydrodynamic simulation. By training on both periodic simulations as well as zooms of overdense environments, we learn the bias of galaxy evolution in differing environments. This allows us to apply the trained model to a larger DMO volume than would be possible if we only trained on a periodic simulation. We demonstrate this application using the $(800 \; \mathrm{Mpc})^3$ P-Millennium simulation, and present predictions for key baryonic distribution functions and clustering statistics from the EAGLE model in this large volume.