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

hal.structure.identifierUniversidad Autónoma de Madrid [UAM]
dc.contributor.authorOLIVARES, J.
hal.structure.identifierM2A 2021
dc.contributor.authorBOUY, H.
dc.contributor.authorSARRO, L. M.
hal.structure.identifierInstitut de Planétologie et d'Astrophysique de Grenoble [IPAG]
dc.contributor.authorMORAUX, E.
dc.contributor.authorBERIHUETE, A.
hal.structure.identifierIstituto di Geologia Ambientale e Geoingegneria [IGAG]
dc.contributor.authorGALLI, P. A. B.
hal.structure.identifierM2A 2021
dc.contributor.authorMIRET-ROIG, N.
dc.date.issued2021-05
dc.identifier.issn0004-6361
dc.description.abstractEnContext. 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%).
dc.description.sponsorshipInitiative d'excellence de l'Université de Bordeaux - ANR-10-IDEX-0003
dc.description.sponsorshipFUTURE - ANR-16-IDEX-0003
dc.language.isoen
dc.publisherEDP Sciences
dc.subject.enproper motions
dc.subject.enmethods: statistical
dc.subject.enopen clusters and associations: general
dc.subject.enopen clusters and associations: individual: M45
dc.title.enMiec: A Bayesian hierarchical model for the analysis of nearby young open clusters
dc.typeArticle de revue
dc.identifier.doi10.1051/0004-6361/202140282
dc.subject.halPhysique [physics]/Astrophysique [astro-ph]
bordeaux.journalAstronomy and Astrophysics - A&A
bordeaux.pageA159
bordeaux.volume649
bordeaux.peerReviewedoui
hal.identifierhal-03295757
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03295757v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Astronomy%20and%20Astrophysics%20-%20A&A&rft.date=2021-05&rft.volume=649&rft.spage=A159&rft.epage=A159&rft.eissn=0004-6361&rft.issn=0004-6361&rft.au=OLIVARES,%20J.&BOUY,%20H.&SARRO,%20L.%20M.&MORAUX,%20E.&BERIHUETE,%20A.&rft.genre=article


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