No abstract

We examine the effectiveness of identifying distinct evolutionary histories in IllustrisTNG-100 galaxies using unsupervised machine learning with Gaussian Mixture Models. We focus on how clustering compressed metallicity histories and star formation histories produces subpopulations of galaxies with distinct evolutionary properties (for both halo mass assembly and merger histories). By contrast, clustering with photometric colours fail to resolve such histories. We identify several populations of interest that reflect a variety of evolutionary scenarios supported by the literature. Notably, we identify a population of galaxies inhabiting the upper-red sequence, M* > 1010M⊙ that has a significantly higher ex-situ merger mass fraction present at fixed masses, and a star formation history that has yet to fully quench, in contrast to an overlapping, satellite-dominated population along the red sequence, which is distinctly quiescent. Extending the clustering to study four clusters instead of three further divides quiescent galaxies, while star forming ones are mostly contained in a single cluster, demonstrating a variety of supported pathways to quenching. In addition to these populations, we identify a handful of populations from our other clusters that are readily applicable to observational surveys, including a population related to post starburst (PSB) galaxies, allowing for possible extensions of this work in an observational context, and to corroborate results within the IllustrisTNG ecosystem.

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 ≤ z ≲ 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.