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Kalkayotl: A cluster distance inference code

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dc.contributor.authorOlivares Romero, Javier
dc.contributor.authorSarro Baro, Luis Manuel
dc.contributor.authorBouy, Hervé
dc.contributor.authorMiret Roig, Nuria
dc.contributor.authorCasamiquela, Laia
dc.contributor.authorGalli, P. A. B.
dc.contributor.authorBerihuete, Ángel
dc.contributor.authorTarricq, Y.
dc.contributor.orcidhttps://orcid.org/0000-0002-7084-487X
dc.contributor.orcidhttps://orcid.org/0000-0001-5292-0421
dc.contributor.orcidhttps://orcid.org/0000-0001-5238-8674
dc.contributor.orcidhttps://orcid.org/0000-0002-8589-4423
dc.date.accessioned2024-06-03T13:09:45Z
dc.date.available2024-06-03T13:09:45Z
dc.date.issued2020-11-24
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/10.1051/0004-6361/202037846
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/10.1051/0004-6361/202037846
dc.description.abstractContext. The high-precision parallax data of the Gaia mission allows for significant improvements in the distance determination to stellar clusters and their stars. In order to obtain accurate and precise distance determinations, systematics such as parallax spatial correlations need to be accounted for, especially with regard to stars in small sky regions. Aims. Our aim is to provide the astrophysical community with a free and open code designed to simultaneously infer cluster parameters (i.e., distance and size) and distances to the cluster stars using Gaia parallax measurements. The code includes cluster-oriented prior families and it is specifically designed to deal with the Gaia parallax spatial correlations. Methods. A Bayesian hierarchical model is created to allow for the inference of both the cluster parameters and distances to its stars. Results. Using synthetic data that mimics Gaia parallax uncertainties and spatial correlations, we observe that our cluster-oriented prior families result in distance estimates with smaller errors than those obtained with an exponentially decreasing space density prior. In addition, the treatment of the parallax spatial correlations minimizes errors in the estimated cluster size and stellar distances, and avoids the underestimation of uncertainties. Although neglecting the parallax spatial correlations has no impact on the accuracy of cluster distance determinations, it underestimates the uncertainties and may result in measurements that are incompatible with the true value (i.e., falling beyond the 2σ uncertainties). Conclusions. The combination of prior knowledge with the treatment of Gaia parallax spatial correlations produces accurate (error < 10%) and trustworthy estimates (i.e., true values contained within the 2σ uncertainties) of cluster distances for clusters up to ∼5 kpc, along with cluster sizes for clusters up to ∼1 kpc.en
dc.description.versionversión publicada
dc.identifier.citationKalkayotl: A cluster distance inference code J. Olivares, L. M. Sarro, H. Bouy, N. Miret-Roig, L. Casamiquela, P. A. B. Galli, A. Berihuete and Y. Tarricq A&A, 644 (2020) A7 DOI: https://doi.org/10.1051/0004-6361/202037846
dc.identifier.doihttps://doi.org/10.1051/0004-6361/202037846
dc.identifier.issn0004-6361 - eISSN 1432-0746
dc.identifier.urihttps://hdl.handle.net/20.500.14468/22256
dc.journal.titleAstronomy & Astrophysics (A&A)
dc.journal.volume644
dc.language.isoen
dc.publisherEDP Sciences
dc.relation.centerFacultades y escuelas::E.T.S. de Ingeniería Informática
dc.relation.departmentInteligencia Artificial
dc.rightsAtribución 4.0 Internacional
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject33 Ciencias Tecnológicas
dc.subject.keywordsmethods: statisticalen
dc.subject.keywordsparallaxesen
dc.subject.keywordsopen clusters and associations: generalen
dc.subject.keywordsstars: distancesen
dc.subject.keywordsvirtual observatory toolsen
dc.titleKalkayotl: A cluster distance inference codeen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
person.familyNameOlivares Romero
person.familyNameSarro Baro
person.givenNameJavier
person.givenNameLuis Manuel
person.identifier.orcid0000-0002-5622-5191
relation.isAuthorOfPublicatione55cff36-187f-49e9-81cb-cbaf28716c53
relation.isAuthorOfPublication9f881bbd-b55d-43bd-87b0-41f8f72cff48
relation.isAuthorOfPublication.latestForDiscoverye55cff36-187f-49e9-81cb-cbaf28716c53
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