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

J Olivares,L M Sarro,P A B Galli,A Berihuete,N Miret-Roig,H Bouy,E Moraux

doi:10.1051/0004-6361/202140282

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 Av ∼ 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%).

Highlights

Stellar clusters are benchmarks against which the predictions of current theories of star formation and evolution can be compared and validated
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
Our methodology can recover members from data sets extincted up to Av ∼ 6 mag with accuracies, true positive, and contamination rates that are better than 99%, 80%, and 9%, respectively

Summary

Introduction

Stellar clusters are benchmarks against which the predictions of current theories of star formation and evolution can be compared and validated In these comparisons, the youngest clusters play an important role because they still hold the imprints of the initial conditions of the molecular cloud from which they were formed. Because of the low star formation efficiency (between 5% and 30%, McKee & Ostriker 2007; Fukushima et al 2020), most of the gas and dust of the parent molecular cloud remains in the vicinity of the newborn stars and extincts their light This extinction can bias the census of candidate members and the population parameters derived from them, the luminosity and mass distributions. To the best of our knowledge, ASteCA is the only membership methodology from the literature that can deal with extinction, though based on theoretical isochrones that are known to face difficulties in reproducing the observed cluster photometric sequences in the low-mass domain (see the discussion in Sect. 5.2 of Bouy et al 2015 and Miret-Roig et al 2019)

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Conclusion