Bayesian Inference of Globular Cluster Properties Using Distribution Functions

Abstract

Abstract We present a Bayesian inference approach to estimating the cumulative mass profile and mean-squared velocity profile of a globular cluster (GC) given the spatial and kinematic information of its stars. Mock GCs with a range of sizes and concentrations are generated from lowered-isothermal dynamical models, from which we test the reliability of the Bayesian method to estimate model parameters through repeated statistical simulation. We find that given unbiased star samples, we are able to reconstruct the cluster parameters used to generate the mock cluster and the cluster’s cumulative mass and mean-squared velocity profiles with good accuracy. We further explore how strongly biased sampling, which could be the result of observing constraints, might affect this approach. Our tests indicate that if we instead have biased samples, then our estimates can be off in certain ways that are dependent on cluster morphology. Overall, our findings motivate obtaining samples of stars that are as unbiased as possible. This may be achieved by combining information from multiple telescopes (e.g., Hubble and Gaia), but will require careful modeling of the measurement uncertainties through a hierarchical model, which we plan to pursue in future work.