Distribution Function-based Modeling of Discrete Kinematic Datasets, in Application to the Milky Way Nuclear Star Cluster

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 where the density profile of tracers and the selection function of the kinematic catalog 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 disk and the Galactic bar. We measure the mass of the black hole to be 4 × 10 6 M ⊙ 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) × 10 7 M ⊙ . We make our models publicly available as part of the Agama software framework for galactic dynamics.