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

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dc.contributor.authorOlivares Romero, Javier
dc.contributor.authorBouy, Hervé
dc.contributor.authorSarro Baro, Luis Manuel
dc.contributor.authorBerihuete, Ángel
dc.contributor.authorP. A. B. Galli
dc.contributor.authorMiret Roig, Nuria
dc.contributor.orcidhttps://orcid.org/0000-0002-7084-487X
dc.contributor.orcidhttps://orcid.org/0000-0003-4127-7295
dc.contributor.orcidhttps://orcid.org/0000-0002-8589-4423
dc.contributor.orcidhttps://orcid.org/0000-0001-5292-0421
dc.date.accessioned2024-05-30T16:55:27Z
dc.date.available2024-05-30T16:55:27Z
dc.date.issued2021-06-01
dc.descriptionThe registered version of this article, first published in Astronomy & Astrophysics (A&A), is available online at the publisher's website: EDP Sciences, https://doi.org/10.1051/0004-6361/202140282
dc.descriptionLa versión registrada de este artículo, publicado por primera vez en Astronomy & Astrophysics (A&A), está disponible en línea en el sitio web del editor: EDP Sciences, https://doi.org/10.1051/0004-6361/202140282
dc.description.abstractContext. 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%).en
dc.description.versionversión publicada
dc.identifier.citationMiec: A Bayesian hierarchical model for the analysis of nearby young open clusters J. Olivares, H. Bouy, L. M. Sarro, E. Moraux, A. Berihuete, P. A. B. Galli and N. Miret-Roig A&A, 649 (2021) A159 DOI: https://doi.org/10.1051/0004-6361/202140282
dc.identifier.doihttps://doi.org/10.1051/0004-6361/202140282
dc.identifier.issn0004-6361 - eISSN 1432-0746
dc.identifier.urihttps://hdl.handle.net/20.500.14468/22236
dc.journal.titleAstronomy & Astrophysics (A&A)
dc.journal.volume649
dc.language.isoen
dc.publisherEDP Sciences
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentInteligencia Artificial
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/deed.es
dc.subject33 Ciencias Tecnológicas
dc.subject.keywordsproper motionsen
dc.subject.keywordsmethods: statisticalen
dc.subject.keywordsopen clusters and associations: generalen
dc.subject.keywordsopen clusters and associations: individual: M45en
dc.titleMiec: A Bayesian hierarchical model for the analysis of nearby young open clustersen
dc.typejournal articleen
dc.typeartículoes
dspace.entity.typePublication
relation.isAuthorOfPublicatione55cff36-187f-49e9-81cb-cbaf28716c53
relation.isAuthorOfPublication9f881bbd-b55d-43bd-87b0-41f8f72cff48
relation.isAuthorOfPublication.latestForDiscoverye55cff36-187f-49e9-81cb-cbaf28716c53
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