A machine learned classifier for RR Lyrae in the VVV survey

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dc.contributor.authorElorrieta, Felipe
dc.contributor.authorEyheramendy, Susana
dc.contributor.authorJordán, Andrés
dc.contributor.authorDékány, István
dc.contributor.authorCatelan, Márcio
dc.contributor.authorAngeloni, Rodolfo
dc.contributor.authorAlonso-García, Javier
dc.contributor.authorContreras-Ramos, Rodrigo
dc.contributor.authorGran, Felipe
dc.contributor.authorHajdu, Gergely
dc.contributor.authorEspinoza, Néstor
dc.contributor.authorSaito, Roberto K.
dc.contributor.authorMinniti, Dante
dc.date.accessioned2023-08-28T21:13:53Z
dc.date.available2023-08-28T21:13:53Z
dc.date.issued2016-11
dc.descriptionIndexación: Scopus.es
dc.description.abstractVariable stars of RR Lyrae type are a prime tool with which to obtain distances to old stellar populations in the Milky Way. One of the main aims of the Vista Variables in the Via Lactea (VVV) near-infrared survey is to use them to map the structure of the Galactic Bulge. Owing to the large number of expected sources, this requires an automated mechanism for selecting RR Lyrae, and particularly those of the more easily recognized type ab (i.e., fundamental-mode pulsators), from the 106−107 variables expected in the VVV survey area. In this work we describe a supervised machine-learned classifier constructed for assigning a score to a Ks-band VVV light curve that indicates its likelihood of being ab-type RR Lyrae. We describe the key steps in the construction of the classifier, which were the choice of features, training set, selection of aperture, and family of classifiers. We find that the AdaBoost family of classifiers give consistently the best performance for our problem, and obtain a classifier based on the AdaBoost algorithm that achieves a harmonic mean between false positives and false negatives of ≈7% for typical VVV light-curve sets. This performance is estimated using cross-validation and through the comparison to two independent datasets that were classified by human experts.es
dc.description.urihttps://www.aanda.org/articles/aa/full_html/2016/11/aa28700-16/aa28700-16.html
dc.identifier.citationAstronomy and Astrophysics. Volume 595. 1 November 2016. Article number A82es
dc.identifier.doi10.1051/0004-6361/201628700
dc.identifier.issn0004-6361
dc.identifier.urihttps://repositorio.unab.cl/xmlui/handle/ria/52873
dc.language.isoenes
dc.publisherEDP Scienceses
dc.subjectStars: Variables: RR Lyraees
dc.subjectMethods: Data Analysises
dc.subjectMethods: Statisticales
dc.subjectTechniques: Photometrices
dc.titleA machine learned classifier for RR Lyrae in the VVV surveyes
dc.typeArtículoes
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