Bayesian Mass Estimates of the Milky Way II: The dark and light sides of parameter assumptions
Gwendolyn M. Eadie, William Harris

TL;DR
This paper uses Bayesian analysis with globular clusters as tracers to estimate the Milky Way's mass profile, exploring how different assumptions influence the results and providing updated mass estimates within various radii.
Contribution
It introduces a comprehensive Bayesian framework to assess the Milky Way's mass, explicitly examining the impact of model assumptions and tracer selection on mass estimates.
Findings
Estimated mass within 125kpc: 5.22×10^11 M_sun
Virial mass estimate: 6.82×10^11 M_sun
Velocity anisotropy parameter β: 0.28
Abstract
We present mass and mass profile estimates for the Milky Way Galaxy using the Bayesian analysis developed by Eadie et al (2015b) and using globular clusters (GCs) as tracers of the Galactic potential. The dark matter and GCs are assumed to follow different spatial distributions; we assume power-law model profiles and use the model distribution functions described in Evans et al. (1997); Deason et al (2011, 2012a). We explore the relationships between assumptions about model parameters and how these assumptions affect mass profile estimates. We also explore how using subsamples of the GC population beyond certain radii affect mass estimates. After exploring the posterior distributions of different parameter assumption scenarios, we conclude that a conservative estimate of the Galaxy's mass within 125kpc is , with a probability region of $(4.79, 5.63)…
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