The Mass of the Milky Way from the H3 Survey

TL;DR

This study uses advanced Bayesian modeling and extensive phase-space data from the H3 Survey and Gaia to estimate the Milky Way's mass, achieving results consistent with recent estimates but highlighting model limitations due to halo substructure.

Contribution

It extends hierarchical Bayesian modeling to incorporate all available 6D phase-space data for more accurate Milky Way mass estimation.

Findings

Median mass within 100 kpc: 0.69 x 10^{12} M_sun

Virial mass estimate: 1.08 x 10^{12} M_sun

Model sensitivity to halo substructure limits precision

Abstract

The mass of the Milky Way is a critical quantity which, despite decades of research, remains uncertain within a factor of two. Until recently, most studies have used dynamical tracers in the inner regions of the halo, relying on extrapolations to estimate the mass of the Milky Way. In this paper, we extend the hierarchical Bayesian model applied in Eadie & Juri\'c (2019) to study the mass distribution of the Milky Way halo; the new model allows for the use of all available 6D phase-space measurements. We use kinematic data of halo stars out to $142~{\rm kpc}$, obtained from the H3 Survey and $\textit{Gaia}$ EDR3, to infer the mass of the Galaxy. Inference is carried out with the No-U-Turn sampler, a fast and scalable extension of Hamiltonian Monte Carlo. We report a median mass enclosed within $100~{\rm kpc}$ of $\rm M(<100 \; kpc) = 0.69_{-0.04}^{+0.05} \times 10^{12} \; M_\odot$ (68% Bayesian credible interval), or a virial mass of $\rm M_{200} = M(<216.2_{-7.5}^{+7.5} \; kpc) = 1.08_{-0.11}^{+0.12} \times 10^{12} \; M_\odot$, in good agreement with other recent estimates. We analyze our results using posterior predictive checks and find limitations in the model's ability to describe the data. In particular, we find sensitivity with respect to substructure in the halo, which limits the precision of our mass estimates to $\sim 15\%$.