Mass models of the Milky Way

TL;DR

This paper introduces a Bayesian method for fitting parametrized mass models of the Milky Way, integrating observational data and theoretical expectations to estimate the Galaxy's mass distribution and its uncertainties.

Contribution

The paper presents a novel Bayesian approach for modeling the Milky Way's mass distribution, providing a comprehensive framework that accounts for observational constraints and theoretical priors.

Findings

Estimated disc scale lengths: 3.00 ± 0.22 kpc (thin), 3.29 ± 0.56 kpc (thick)

Solar radius: 8.29 ± 0.16 kpc, circular speed: 239 ± 5 km/s

Total stellar mass: 6.43 ± 0.63 × 10^10 M_sun, virial mass: 1.26 ± 0.24 × 10^12 M_sun

Abstract

We present a simple method for fitting parametrized mass models of the Milky Way to observational constraints. We take a Bayesian approach which allows us to take into account input from photometric and kinematic data, and expectations from theoretical modelling. This provides us with a best-fitting model, which is a suitable starting point for dynamical modelling. We also determine a probability density function on the properties of the model, which demonstrates that the mass distribution of the Galaxy remains very uncertain. For our choices of parametrization and constraints, we find disc scale lengths of 3.00 \pm 0.22 kpc and 3.29 \pm 0.56 kpc for the thin and thick discs respectively; a Solar radius of 8.29 \pm 0.16 kpc and a circular speed at the Sun of 239 \pm 5 km/s; a total stellar mass of 6.43 \pm 0.63 * 10^10 M_sun; a virial mass of 1.26 \pm 0.24 * 10^12 M_sun and a local dark matter density of 0.40 \pm 0.04 GeV/cm^3. We find some correlations between the best-fitting parameters of our models (for example, between the disk scale lengths and the Solar radius), which we discuss. The chosen disc scale-heights are shown to have little effect on the key properties of the model.