Estimating the Milky Way's Mass via Hierarchical Bayes: A Blind Test on
  MUGS2 Simulated Galaxies
  In a series of three papers, Eadie et al. developed a hierarchical Bayesian
method to estimate the Milky Way Galaxy's mass given a physical model for the
potential, a measurement model, and kinematic data of test particles such as
globular clusters (GCs) or halo stars in the Galaxy's halo. The Galaxy's virial
mass was found to have a 95\% Bayesian credible region (c.r.) of $(0.67, 1.09)
\times 10^{12} M_{\odot}$. In the present study, we test the hierarchical
Bayesian method against simulated galaxies created in the McMaster Unbiased
Galaxy Simulations 2 (MUGS2), for which the true mass is known. We estimate the
masses of MUGS2 galaxies using GC analogs from the simulations as tracers. The
analysis, completed as a blind test, recovers the true $M_{200}$ of the MUGS2
galaxies within 95\% Bayesian c.r. in 8 out of 18 cases. Of the 10 galaxy
masses that were not recovered within the 95\% c.r., a large subset have
posterior distributions that occupy extreme ends of the parameter space allowed
by the priors. A few incorrect mass estimates are explained by the exceptional
evolution history of the galaxies. We also find evidence that the model cannot
describe both the galaxies' inner and outer structure simultaneously in some
cases. After removing the GC analogs associated with the galactic disks, the
true masses were found more reliably (13 out of 18 were predicted within the
c.r.). Finally, we discuss how representative the GC analogs are of the real GC
population in the Milky Way.
