DIRAC Researcher, Gwendolyn Eadie, published a paper in the Astrophysical Journal.
Published on September 24, 2018.
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) × 1012 . 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.