Using a minimally parametrised SHAM to constrain the link between dark matter and galaxies
F. L. Davidson (1), D. Bacon (1) A. Amara (2), K. Koyama (1), W. G., Hartley (3), L. F. de la Bella (4), S. I. Tam (5), K. Umetsu (6), J. Noller, (7) ((1) Institute of Cosmology, Gravitation Portsmouth UK, (2) School of, Mathematics, Physics Surrey UK

TL;DR
This paper employs a minimally parametrized empirical SHAM model to connect dark matter halos with galaxy properties, constraining key parameters to match observed galaxy distributions and improve understanding of galaxy-halo relationships.
Contribution
It introduces a simplified SHAM model with three parameters, applying Bayesian methods to fit galaxy data and providing new constraints on galaxy-halo connection parameters.
Findings
Best-fit parameters for two galaxy samples are tightly constrained.
Model aligns well with existing literature for central galaxies.
Future work will refine satellite galaxy normalization.
Abstract
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: (halo mass where half the galaxies assigned should be quenched), (transition width from star forming to quenched) and (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…
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Taxonomy
TopicsScientific Research and Discoveries · Computational Physics and Python Applications · Astronomy and Astrophysical Research
