Modelling the galaxy-halo connection with semi-recurrent neural networks

TL;DR

This paper introduces a semi-recurrent neural network model trained on IllustrisTNG data to predict galaxy evolution properties from dark matter halo characteristics, capturing key galaxy-halo connection features.

Contribution

The paper presents a novel neural network architecture combining static and dynamic halo properties to accurately model galaxy star formation and metallicity histories.

Findings

Successfully reproduces stellar-to-halo mass relation

Captures galaxy colour bimodality and downsizing trends

Matches observational galaxy colour and magnitude distributions

Abstract

We present an artificial neural network design in which past and present-day properties of dark matter halos and their local environment are used to predict time-resolved star formation histories and stellar metallicity histories of central and satellite galaxies. Using data from the IllustrisTNG simulations, we train a TensorFlow-based neural network with two inputs: a standard layer with static properties of the dark matter halo, such as halo mass and starting time; and a recurrent layer with variables such as overdensity and halo mass accretion rate, evaluated at multiple time steps from $0 \leq z \lesssim 20$. The model successfully reproduces key features of the galaxy halo connection, such as the stellar-to-halo mass relation, downsizing, and colour bimodality, for both central and satellite galaxies. We identify mass accretion history as crucial in determining the geometry of the star formation history and trends with halo mass such as downsizing, while environmental variables are important indicators of chemical enrichment. We use these outputs to compute optical spectral energy distributions, and find that they are well matched to the equivalent results in IllustrisTNG, recovering observational statistics such as colour bimodality and mass-magnitude diagrams.