First Light and Reionisation Epoch Simulations (FLARES) XVII: Learning the galaxy-halo connection at high redshifts

TL;DR

This paper develops a machine learning approach to model the galaxy-halo connection at high redshifts using large, high-resolution simulations, enabling the creation of extensive mock galaxy catalogs beyond current simulation limits.

Contribution

It introduces a novel machine learning framework that captures the galaxy-halo relationship across diverse environments, overcoming the volume limitations of hydrodynamical simulations.

Findings

The model accurately predicts galaxy properties like stellar mass, star formation rate, metallicity, and size.

It maintains performance down to dark matter particle resolutions of ~10^8 M_Sun.

The approach enables large-scale mock catalog creation with minimal resolution loss.

Abstract

Understanding the galaxy-halo relationship is not only key for elucidating the interplay between baryonic and dark matter, it is essential for creating large mock galaxy catalogues from N-body simulations. High-resolution hydrodynamical simulations are limited to small volumes by their large computational demands, hindering their use for comparisons with wide-field observational surveys. We overcome this limitation by using the First Light and Reionisation Epoch Simulations (FLARES), a suite of high-resolution (M_gas = 1.8 x 10^6 M_Sun) zoom simulations drawn from a large, (3.2 cGpc)^3 box. We use an extremely randomised trees machine learning approach to model the relationship between galaxies and their subhaloes in a wide range of environments. This allows us to build mock catalogues with dynamic ranges that surpass those obtainable through periodic simulations. The low cost of the zoom simulations facilitates multiple runs of the same regions, differing only in the random number seed of the subgrid models; changing this seed introduces a butterfly effect, leading to random differences in the properties of matching galaxies. This randomness cannot be learnt by a deterministic machine learning model, but by sampling the noise and adding it post-facto to our predictions, we are able to recover the distributions of the galaxy properties we predict (stellar mass, star formation rate, metallicity, and size) remarkably well. We also explore the resolution-dependence of our models' performances and find minimal depreciation down to particle resolutions of order M_DM ~ 10^8 M_Sun, enabling the future application of our models to large dark matter-only boxes.