Applying unsupervised learning to resolve evolutionary histories and
  explore the galaxy-halo connection in IllustrisTNG
  We examine the effectiveness of identifying distinct evolutionary histories
in IllustrisTNG-100 galaxies using unsupervised machine learning with Gaussian
Mixture Models on galaxy observables. 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
ex-situ mass fraction). By contrast, clustering with photometric colours fails
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, $M_{*}>10^{10} M_{\odot}$ 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 constrained to 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, allowing for possible
extensions of this work in an observational context, and to corroborate results
within the IllustrisTNG ecosystem.
