UC Riverside

We have developed a post-processing tagging technique to model globular clusters (GCs) in cosmological hydrodynamical simulations. We have applied our method to the Illustris and TNG50 simulations to study several aspects of GCs in galaxy groups and clusters, regimes where no other theoretical technique is available to link galaxies, halos and GCs. We find that GCs are good tracers of dark matter–both in terms of their radial distribution and shape to trace the host halos in groups and clusters, but also through their kinematics to constrain the dynamical mass of dwarf galaxies. We have used our catalogs to establish one-to-one comparison to observational determinations of dark matter mass in dwarfs, finding that while systems with more than 10 GC may recover the right dark matter mass content via Jeans modeling and other derived mass estimators, for dwarfs with less than 10 GCs, assumptions in the prior and different methods to estimate the velocity dispersion may heavily bias the ability to infer dark matter mass from kinematics. We find that dwarf galaxies are consistent with populating an extrapolation of a single power-law relation between GC mass and halo mass observed for more massive systems, at least all the way down to dwarfs comparable to dSphs in the Local Group (M∗ = 5×106M⊙). Lastly, we explore the GC systems of the set of morphologically defined ultra-diffuse galaxies (UDGs) within the TNG50 simulation. Observationally, the kinematics of the GC systems of UDGs show a large diversity, with systems ranging from apparent “failed galaxies” living in overly- massive dark matter halos to the opposite extreme, where UDGs are seemingly dark-matter free. We use our GC catalog to demonstrate that much of this diversity, in particular towards low GC numbers, might arise as a combination of a low number of dynamical tracers coupled to ongoing tidal disruption—in agreement with evidence of stellar streams in some of the UDGs with low velocity dispersion—as well as contamination from GCs in the intracluster medium.