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).
The Astrophysical Journal
The American Astronomical Society, The Institute of Physics
Grant or Award Name
NASA grant NNX11AF34G, NSF grant DMS 1208791
Scholarly Commons Citation
Stenning, D., von Hippel, T., Wagner-Kaiser, R., Robinson, E., van Dyk, D., Sarajedini, A., & Stein, N. (2018). Bayesian Analysis of Two Stellar Populations in Galactic Globular Clusters I: Statistical and Computational Methods. The Astrophysical Journal, 826(1). https://doi.org/10.3847/0004-637X/826/1/41