Distribution function-based modelling of discrete kinematic datasets, in application to the Milky Way nuclear star cluster

TL;DR

This paper introduces a distribution function-based method for modeling stellar systems using discrete kinematic data, applied to the Milky Way's nuclear star cluster to estimate black hole and stellar mass profiles.

Contribution

The authors develop an improved modeling approach that constrains mass distributions from discrete kinematic data, demonstrated on the Milky Way's nuclear star cluster.

Findings

Black hole mass estimated at 4 million solar masses with 10% uncertainty.

Total mass within 10 pc constrained to (2.0-2.3) x 10^7 solar masses.

Method and models are publicly available in the Agama software framework.

Abstract

We present a method for constructing dynamical models of stellar systems described by distribution functions and constrained by discrete-kinematic data. We implement various improvements compared to earlier applications of this approach, demonstrating with several examples that it can deliver meaningful constraints on the mass distribution even in situations when the density profile of tracers and the selection function of the kinematic catalogue are unknown. We then apply this method to the Milky Way nuclear star cluster, using kinematic data (line-of-sight velocities and proper motions) for a few thousand stars within 10 pc from the central black hole, accounting for the contributions of the nuclear stellar disc and the Galactic bar. We measure the mass of the black hole to be 4x10^6 Msun with a 10% uncertainty, which agrees with the more precise value obtained by the GRAVITY instrument. The inferred stellar mass profile depends on the choice of kinematic data, but the total mass within 10 pc is well constrained in all models to be (2.0-2.3)x10^7 Msun. We make our models publicly available as part of the Agama software framework for galactic dynamics.