Estimating the Milky Way's Mass via Hierarchical Bayes: A Blind Test on MUGS2 Simulated Galaxies

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 $(0.67,1.09)\times 10^{12}{M}_{\odot }$. 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.

Published in ApJ. 17 pages, 12 figures, 4 tables

References in corpus (12)

• Wilkinson Microwave Anisotropy Probe (WMAP) Three Year Results: Implications for Cosmology
• The EAGLE project: Simulating the evolution and assembly of galaxies and their environments
• Introducing the Illustris Project: Simulating the coevolution of dark and visible matter in the Universe
• The mass distribution and gravitational potential of the Milky Way
• The Milky Way's Circular Velocity Curve to 60 kpc and an Estimate of the Dark Matter Halo Mass from Kinematics of ~2400 SDSS Blue Horizontal Branch Stars
• "Skinny Milky Way, Please", says Sagittarius
• A Superbubble Feedback Model for Galaxy Simulations
• The velocity dispersion and mass profile of the Milky Way
• Dynamics of the Magellanic Clouds in a LCDM Universe
• Balancing mass and momentum in the Local Group
• Bayesian Mass Estimates of the Milky Way II: The dark and light sides of parameter assumptions
• Bayesian Mass Estimates of the Milky Way: including measurement uncertainties with hierarchical Bayes

Cited by in corpus (16)

• The Milky Way total mass profile as inferred from Gaia DR2
• The mass of our Milky Way
• The cumulative mass profile of the Milky Way as determined by globular cluster kinematics from Gaia DR2
• The mass of the Milky Way out to 100 kpc using halo stars
• The Rotation Curve, Mass Distribution and Dark Matter Content of the Milky Way from Classical Cepheids
• The Mass of the Milky Way from the H3 Survey
• Milky Way Mass with K Giants and BHB Stars Using LAMOST, SDSS/SEGUE, and Gaia: 3D Spherical Jeans Equation and Tracer Mass Estimator
• Dynamical Histories of the Crater II and Hercules Dwarf Galaxies
• Mass models of the Milky Way and estimation of its mass from the GAIA DR3 data-set
• HALO7D II: The Halo Velocity Ellipsoid and Velocity Anisotropy with Distant Main Sequence Stars
• Milky Way archaeology using RR Lyrae and type II Cepheids II. High velocity RR Lyrae stars, and mass of the Milky Way
• Fast, Scalable Approximations to Posterior Distributions in Extended Latent Gaussian Models
• Galactic Mass Estimates using Dwarf Galaxies as Kinematic Tracers
• Non-Parametric Spherical Jeans Mass Estimation with B-splines
• Bringing Stellar Evolution & Feedback Together: Summary of proposals from the Lorentz Center Workshop, 2022
• Hierarchical Bayesian Inference of Globular Cluster Properties