Bayesian Mass Estimates of the Milky Way: The Dark and Light Sides of Parameter Assumptions

Abstract

We present mass and mass profile estimates for the Milky Way (MW) Galaxy using the Bayesian analysis developed by Eadie et al. 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. and Deason et al. 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 125 kpcis5.22 10 11  M , with a 50% probability region of4. ). If we consider only the GCs beyond 10 kpc, then the virial mass iś 9.02 5.69, 10.86 10 11 ( )vir 24 19 kpc). We also arrive at an estimate of the velocity anisotropy parameter β of the GC population, which is b = 0.28 with a 50% credible region (0.21, 0.35). Interestingly, the mass estimates are sensitive to both the dark matter halo potential and visible matter tracer parameters, but are not very sensitive to the anisotropy parameter.

Galactic rotation dynamics in f(T) gravity

“…The total mass of these contributions to the galaxy obtained in this work agrees with the order of the total masses from both Refs. [35,36]. Furthermore, the values of the total mass, and the disk mass obtained here even fall within the error bars of the respective masses given by Ref.…”

“…Ref. [36] reports a total combined mass of 5.22 × 10 11 M of the Milky Way which includes the dark matter contribution. In Ref.…”

Bayesian Mass Estimates of the Milky Way: Including Measurement Uncertainties with Hierarchical Bayes

“…The new hierarchical Bayesian approach further improves the framework presented by Eadie et al (2015b); Eadie & Harris (2016) and builds upon the preliminary reports by Eadie et al (2015a,c). The method uses a distribution function f (E, L) 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 the results are similar but more strongly constrained. Next, we take advantage of the new statistical framework and incorporate all possible GC data, finding a cumulative mass profile with Bayesian credible regions.…”

Globular Clusters of the Galaxy: Chemical Composition vs Kinematics

“…Differences in the abundances of α-elements in the stellar objects of the Galaxy and the surrounding low-mass dwarf satellite galaxies confirm the wellknown conclusion that all globular clusters and field stars of the accreted halo are remnants of galaxies of higher mass than the current environment of the Galaxy. A possible exception is a distant low-metal cluster with low relative abundance of α-elements Rup 106. stars and dwarf satellite galaxies, which we use for comparison.For all 157 globular clusters, we calculated the rectangular coordinates from positions and distances from [7], and for 115 of them-from [8]. We supplemented the data for the last 115 clusters with cylindrical velocity components, taking them from the authors of [8], and for the clusters Ter 4, Pal 3, Pal 5, Pal 13, NGC6528 and NGC7006 calculated them from their proper motions, radial velocities and distances from [7].…”