Bayesian Mass Estimates of the Milky Way: including measurement uncertainties with hierarchical Bayes
Gwendolyn Eadie, Aaron Springford, William Harris

TL;DR
This paper introduces a hierarchical Bayesian method for estimating the Milky Way's mass profile that effectively incorporates measurement uncertainties and incomplete data, providing more constrained and comprehensive mass estimates.
Contribution
The paper develops a novel hierarchical Bayesian framework that improves mass estimation of the Milky Way by including measurement uncertainties and all available data types.
Findings
Mass within 125 kpc is estimated at 4.8 x 10^11 solar masses.
The method yields more constrained mass profiles compared to previous approaches.
Remote tracers with complete velocity data significantly influence mass estimates.
Abstract
We present a hierarchical Bayesian method for estimating the total mass and mass profile of the Milky Way Galaxy. The new hierarchical Bayesian approach further improves the framework presented by Eadie, Harris, & Widrow (2015) and Eadie & Harris (2016) and builds upon the preliminary reports by Eadie et al (2015a,c). The method uses a distribution function to model the galaxy and kinematic data from satellite objects such as globular clusters (GCs) to trace the Galaxy's gravitational potential. A major advantage of the method is that it not only includes complete and incomplete data simultaneously in the analysis, but also incorporates measurement uncertainties in a coherent and meaningful way. We first test the hierarchical Bayesian framework, which includes measurement uncertainties, using the same data and power-law model assumed in Eadie & Harris (2016), and find…
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