Inferring the Properties of Galaxies and Dark Matter Halos from the Dynamics of Their Stars and Star Clusters
There is a long and rewarding history in astronomy of studying the motions of stars, gas, and planets to learn about the distribution of mass in the universe. Observations of nearby galaxies rotating faster than otherwise expected are one of the pillars of evidence for the cold dark matter cosmological model. As increasingly precise predictions are made about the small scale distribution of mass in the universe, we need tools to be able to weigh our beliefs about the mass content of galaxies and how they can be updated by increasingly richer and more complete observations. In this thesis I present a series of applications of a Bayesian hierarchical model for the equilibrium dynamics of spherical galaxies. Some key features of the model are: a natural mechanism for propagating the systematic uncertainty of parameters such as orbital anisotropy, distance, and stellar mass-to-light ratio into estimates for the dark matter content of galaxies, the ability to jointly model multiple tracer populations with heterogeneous data, and a clear way to incorporate and examine prior assumptions about galaxy and dark matter scaling relations.