BAYESIAN ANALYSIS OF TWO STELLAR POPULATIONS IN GALACTIC GLOBULAR CLUSTERS I: STATISTICAL AND COMPUTATIONAL METHODS
File(s)
Published version
Accepted version
Author(s)
Type
Journal Article
Abstract
We develop a Bayesian model for globular clusters composed of multiple stellar populations, extending
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).
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).
Date Issued
2016-07-20
Date Acceptance
2016-04-19
Citation
Astrophysical Journal, 2016, 826
ISSN
1538-4357
Publisher
American Astronomical Society
Journal / Book Title
Astrophysical Journal
Volume
826
Copyright Statement
©2016 IOP Publishing Ltd.
Sponsor
The Royal Society
Commission of the European Communities
Commission of the European Communities
Grant Number
WM110023
FP7-PEOPLE-2012-CIG-321865
691164
Subjects
Science & Technology
Physical Sciences
Astronomy & Astrophysics
globular clusters: general
globular clusters: individual (NGC 5272)
methods: data analysis
methods: statistical
COLOR-MAGNITUDE DIAGRAMS
MILKY-WAY
OMEGA-CENTAURI
EVOLUTION
PARAMETERS
AGES
NGC-2808
CATALOG
HALO
0201 Astronomical And Space Sciences
0305 Organic Chemistry
0306 Physical Chemistry (Incl. Structural)
Publication Status
Published
Article Number
41