Using a minimally parametrised SHAM to constrain the link between dark matter and galaxies

Models of the galaxy-halo connection are needed to understand both galaxy clusters and large scale structure. To make said models, we need a robust method that assigns galaxies to halos and matches the observed and simulated stellar-halo mass relation. We employ an empirical Subhalo Abundance Matching (SHAM) model implemented in the halos module of SkyPy which assigns blue and red galaxies based on the Peng et al. (2010) (arXiv:1003.4747v2) model containing three parameters: $M_μ$ (halo mass where half the galaxies assigned should be quenched), $σ$ (transition width from star forming to quenched) and $b$ (baseline quenched fraction at low mass). We test two sets of galaxy stellar mass functions for four populations of galaxies (central/satellite, blue/red) and run parameter estimation using Approximate Bayesian Computation over each model when compared to a set of applicable literature models. For the Weigel et al. (2016) (arXiv:1604.00008v1) galaxies we find best fit values of log $M_μ= 11.94^{+0.02}_{-0.02}$, $σ= 0.49^{+0.04}_{-0.04}$ and $b = 0.31^{+0.01}_{-0.01}$. For the Birrer et al. (2014) (arXiv:1401.3162v2) galaxies we find best fit values of log $M_μ= 11.93^{+0.01}_{-0.01}$, $σ= 0.53^{+0.04}_{-0.04}$ and $b = 0.51^{+0.05}_{-0.04}$. Overall, we demonstrate that these constraints produce a model that is consistent with literature models for the central galaxies. Future research will focus on the normalisation of the satellite galaxies in order to better constrain the $b$ parameter.