Galaxies and Cosmology Seminar

Connor Painter, The University of Texas at Austin

Towards the Characterization of Proto-Globular Clusters with BonFIRE

JWST observations of high-redshift galaxies have often revealed compact or clumpy morphologies, indicating that star clusters may be dominating the galaxy’s light. Coincidentally, it has recently become computationally plausible to explicitly resolve the formation of star clusters within large-volume cosmological simulations. I discuss the state of the field of globular cluster (GC) simulation and suggest BonFIRE, a new large-volume FIRE-3 simulation, as a testing grounds for proto-GC formation. BonFIRE takes advantage of FIRE-3 prescriptions for the explicitly-resolved multiphase interstellar medium and state-of-the-art stellar feedback models to simulate a cosmological sample of high-redshift galaxies with mass resolution high enough to resolve collapsing giant molecular clouds. In post-processing, thousands of infant star clusters can be catalogued and characterized to offer predictions for future deep observations of high-redshift galaxies

Maria Straight, The University of Texas at Austin

CMB Limits on Dark Matter-Proton Scattering Using Profile Likelihoods

While direct and indirect detection experiments have yet to find dark matter interacting with standard model particles, cosmological probes provide a complementary approach for exploring phenomenological dark matter-baryon scattering models. These models have two parameters vulnerable to prior volume effects, namely the scattering cross section and the fraction of dark matter that interacts with standard model particles. As either parameter approaches its standard model value, the prior volume becomes unconstrained, potentially biasing a Bayesian posterior distribution towards that region. To avoid the use of priors, we take a frequentist approach using profile likelihoods to constrain dark matter-proton scattering using observations of the cosmic microwave background. We find no evidence of interactions between dark matter and protons, which is consistent with the Bayesian analysis. For different fractions of dark matter interacting with protons, we obtain upper limits on the interaction cross section as a function of dark matter particle mass, which we compare to the Bayesian limits.