Dissertation: Lights in Dark Places: Inferring the Milky Way Mass Profile using Galactic Satellites and Hierarchical Bayes

(Cassiopeia – Autumn/l’automne 2017)

by Gwendolyn Eadie
Thesis defended on July 18, 2017
Department of Physics and Astronomy, McMaster University
Thesis advisor: Dr. William Harris

Despite valiant efforts by astronomers, the mass of the Milky Way (MW) Galaxy is poorly constrained, and not known within a factor of two. A range of techniques have been developed and different types of data have been used to estimate the MW’s mass. One of the most promising and popular techniques is to use the velocity and position information of satellite objects orbiting the Galaxy to infer the gravitational potential, and thus the total mass. Using these satellites, or Galactic tracers, presents a number of challenges: 1) much of the tracer velocity data are incomplete (i.e. only line-of-sight velocities have been measured), 2) our position in the Galaxy complicates how we quantify measurement uncertainties of mass estimates, and 3) the amount of available tracer data at large distances, where the dark matter halo dominates, is small. The latter challenge will improve with current and upcoming observational programs such as Gaia and the Large Synoptic Survey Telescope (LSST), but to properly prepare for these data sets we must overcome the former two. In this thesis work, we have created a hierarchical Bayesian framework to estimate the Galactic mass profile. The method includes incomplete and complete data simultaneously, and incorporates measurement uncertainties through a measurement model. The physical model relies on a distribution function for the tracers that allows the tracer and dark matter to have different spatial density profiles. When the hierarchical Bayesian model is confronted with the kinematic data from satellites, a posterior distribution is acquired and used to infer the mass and mass profile of the Galaxy.

This thesis walks through the incremental steps that led to the development of the hierarchical Bayesian method, and presents MW mass estimates when the method is applied to the MW’s globular cluster population. Our best estimate of the MW’s virial mass is M(vir) = 0.87 x 1012 Solar masses with a 95% credible range of (0.67 – 1.09) x 1012 Solar masses. We also present preliminary results from a blind test on hydrodynamical, cosmological computer-simulated MW-type galaxies from the McMaster Unbiased Galaxy Simulations. These results suggest our method may be able to reliably recover the virial mass of the Galaxy.

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