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 the cosmological implications of measurements of void-galaxy and galaxy-galaxy clustering from the Sloan Digital Sky Survey (SDSS) Main Galaxy Sample (MGS), Baryon Oscillation Spectroscopic Survey (BOSS), and extended BOSS (eBOSS) luminous red galaxy catalogues from SDSS Data Release 7, 12, and 16, covering the redshift range 0.07 < z < 1.0. We fit a standard ΛCDM cosmological model as well as various extensions including a constant dark energy equation of state not equal to −1, a time-varying dark energy equation of state, and these same models allowing for spatial curvature. Results on key parameters of these models are reported for void-galaxy and galaxy-galaxy clustering alone, both of these combined, and all these combined with measurements from the cosmic microwave background (CMB) and supernovae (SN). For the combination of void-galaxy and galaxy-galaxy clustering, we find tight constraints of Ωm = 0.356 ± 0.024 for a base ΛCDM cosmology, $\Omega _\mathrm{m} = 0.391^{+0.028}_{-0.021}, w = -1.50^{+0.43}_{-0.28}$ additionally allowing the dark energy equation of state w to vary, and $\Omega _\mathrm{m} = 0.331^{+0.067}_{-0.094}, w=-1.41^{+0.70}_{-0.31},\ \mathrm{and}\ \Omega _\mathrm{k} = 0.06^{+0.18}_{-0.13}$ further extending to non-flat models. The combined SDSS results from void-galaxy and galaxy-galaxy clustering in combination with CMB+SN provide a 30% improvement in parameter Ωm over CMB+SN for ΛCDM, a 5% improvement in parameter Ωm when w is allowed to vary, and a 32% and 68% improvement in parameters Ωm and Ωk when allowing for spatial curvature.