A. Berihuete Macías, J. Olivares, L. M. Sarro, H. Bouy, E. Moraux
It is known that the majority of the stars forms and evolves in stellar
clusters (Lada & Lada 1995), however, only about 10% of the star-forming
regions remain bounded in the form of a cluster. Therefore, to understand the
formation process of the majority of stars, it is crucial to fully decode the
early evolution of stellar clusters by obtaining a complete and unbiased
census of their members as well as precise estimates of their physical
properties.
We present a Bayesian hierarchical model that simultaneously obtain membership
probabilities and parameters of distributions that describe the physical
properties of the clusters. The Bayesian hierarchical model
has the advantages of dealing with both uncertainties and missing data as well
as obtaining the full probability distribution of the parameters and the
membership probabilities. This methodology has been benchmarked previously
with DANCe data of the Pleiades cluster in Bouy et al. (2013) and Sarro et
al. (2014).
Palabras clave: Bayes, hierarchical models, astrostatistics, open clusters
Programado
L06.2 Grupo de Inferencia Bayesiana: Bayesian model selection in Classification and Clustering
5 de septiembre de 2016 12:55
0.09 - Aula de proyectos 2
