Estimating the Milky Way's Mass via Hierarchical Bayes: A Blind Test on MUGS2 Simulated Galaxies
Gwendolyn M. Eadie, Benjamin W. Keller, William E. Harris

TL;DR
This study tests a hierarchical Bayesian method for estimating galaxy mass using simulated data, demonstrating its effectiveness and limitations in recovering true masses within credible intervals.
Contribution
The paper provides a blind test of a hierarchical Bayesian approach on simulated galaxies, highlighting its accuracy, challenges, and potential improvements for Milky Way mass estimation.
Findings
Successfully recovered true galaxy masses in 8 out of 18 cases within 95% credible intervals.
Removing disk-associated tracers improved mass estimation accuracy.
Identified limitations of the model in describing galaxy structure simultaneously.
Abstract
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 . 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 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\%…
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