http://arxiv.org/abs/2112.12516
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.
T. Fraser, R. Tojeiro and H. Chittenden
Fri, 24 Dec 21
24/58
Comments: 6 figures, 4 tables, 12 pages. Submitted to Monthly Notices of the Royal Astronomical Society (MNRAS). Appendix contains 6 additional tables and 15 additional figures across 11 pages
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