Bayesian Analysis of Two Stellar Populations in Galactic Globular Clusters I: Statistical and Computational Methods

We develop a Bayesian model for globular clusters composed of multiple stellar populations, extend- ing earlier statistical models for open clusters composed of simple (single) stellar populations (e.g., van Dyk et al. 2009; Stein et al. 2013). Specifically, we model globular clusters with two populations that differ in helium abundance. Our model assumes a hierarchical structuring of the parameters in which physical properties|age, metallicity, helium abundance, distance, absorption, and initial mass|are common to (i) the cluster as a whole or to (ii) individual populations within a cluster, or are unique to (iii) individual stars. An adaptive Markov chain Monte Carlo (MCMC) algorithm is devised for model fitting that greatly improves convergence relative to its precursor non-adaptive MCMC algorithm. Our model and computational tools are incorporated into an open-source software suite known as BASE-9. We use numerical studies to demonstrate that our method can recover parameters of two- population clusters, and also show model misspecification can potentially be identified. As a proof of concept, we analyze the two stellar populations of globular cluster NGC 5272 using our model and methods. (BASE-9 is available from GitHub: https://github.com/argiopetech/base/releases).