Miec: A Bayesian hierarchical model for the analysis of nearby young open clusters

¹ Laboratoire d’astrophysique de Bordeaux, Univ. Bordeaux, CNRS, B18N, allée Geoffroy Saint-Hilaire, 33615 Pessac, France
e-mail: javier.olivares-romero@u-bordeaux.fr
² Depto. de Inteligencia Artificial, UNED, Juan del Rosal, 16, 28040 Madrid, Spain
³ Univ. Grenoble Alpes, CNRS, IPAG, 38000 Grenoble, France
⁴ Depto. Estadística e Investigación Operativa. Universidad de Cádiz, Avda. República Saharaui s/n, 11510 Puerto Real, Cádiz, Spain

Received: 3 January 2021
Accepted: 16 February 2021

Abstract

Context. The analysis of luminosity and mass distributions of young stellar clusters is essential to understanding the star-formation process. However, the gas and dust left over by this process extinct the light of the newborn stars and can severely bias both the census of cluster members and itsss luminosity distribution.

Aims. We aim to develop a Bayesian methodology to infer, with minimal biases due to photometric extinction, the candidate members and magnitude distributions of embedded young stellar clusters.

Methods. We improve a previously published methodology and extend its application to embedded stellar clusters. We validate the method using synthetically extincted data sets of the Pleiades cluster with varying degrees of extinction.

Results. Our methodology can recover members from data sets extincted up to A_v ∼ 6 mag with accuracies, true positive, and contamination rates that are better than 99%, 80%, and 9%, respectively. Missing values hamper our methodology by introducing contaminants and artifacts into the magnitude distributions. Nonetheless, these artifacts vanish through the use of informative priors in the distribution of the proper motions.

Conclusions. The methodology presented here recovers, with minimal biases, the members and distributions of embedded stellar clusters from data sets with a high percentage of sources with missing values (> 96%).