Tristan Fraser

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 NMF-compressed metallicity histories and star formation histories produces subpopulations of galaxies with distinct evolutionary properties. This is in contrast to clustering photometric colours, which fail to resolve such histories. We identify a population of galaxies inhabiting the upper-red sequence, that has a significantly higher ex-situ merger mass fraction, 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. These populations also have distinct halo evolutionary histories, demonstrating the potential of applying unsupervised learning to observable properties of galaxies to distinguish between these histories.