J/A+A/642/A58ivo://CDS.VizieR/J/A+A/642/A58doi:10.26093/cds/vizier.36420058CDSCabral J.B.Ramos F.Gurovich S.Granitto P.M.2022-09-02T09:03:57Z2020-11-20T10:24:25ZCDS support team

CDS, Observatoire de Strasbourg, 11 rue de l'Universite, F-67000 Strasbourg, France

cds-question@unistra.frvariable-starsThe creation of a 3D map of the bulge using RR Lyrae (RRL) is one of the main goals of the VISTA Variables in the Via Lactea Survey (VVV) and VVV(X) surveys. The overwhelming number of sources undergoing analysis undoubtedly requires the use of automatic procedures. In this context, previous studies have introduced the use of machine learning (ML) methods for the task of variable star classification. Our goal is to develop and test an entirely automatic ML-based procedure for the identification of RRLs in the VVV Survey. This automatic procedure is meant to be used to generate reliable catalogs integrated over several tiles in the survey. Following the reconstruction of light curves, we extracted a set of period- and intensity-based features, which were already defined in previous works. Also, for the first time, we put a new subset of useful color features to use. We discuss in considerable detail all the appropriate steps needed to define our fully automatic pipeline, namely: the selection of quality measurements; sampling procedures; classifier setup, and model selection. As a result, we were able to construct an ensemble classifier with an average recall of 0.48 and average precision of 0.86 over 15 tiles. We also made all our processed datasets available and we published a catalog of candidate RRLs. Perhaps most interestingly, from a classification perspective based on photometric broad-band data, our results indicate that color is an informative feature type of the RRL objective class that should always be considered in automatic classification methods via ML. We also argue that recall and precision in both tables and curves are high-quality metrics with regard to this highly imbalanced problem. Furthermore, we show for our VVV data-set that to have good estimates, it is important to use the original distribution more abundantly than reduced samples with an artificial balance. Finally, we show that the use of ensemble classifiers helps resolve the crucial model selection step and that most errors in the identification of RRLs are related to low-quality observations of some sources or to the increased difficulty in resolving the RRL-C type given the data.2020A&A...642A..58Chttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/642/A58CatalogResearchIsServedByTAP VizieR generic serviceIsServedByConesearch servicerelated-toII/348 : VISTA Variable in the Via Lactea Survey DR2 (Minniti+, 2017)https://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/A+A/642/A58https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/A+A/642/A58http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/A+A/642/A58https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/A+A/642/A58https://vizier.iucaa.in/viz-bin/votable?-source=J/A+A/642/A58http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/A+A/642/A58GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapCone search capability for table J/A+A/642/A58/tableg (Candidates to RRL sorted by probability of being an RRL)https://vizier.cds.unistra.fr/viz-bin/conesearch/J/A+A/642/A58/tableg?https://vizier.iucaa.in/viz-bin/conesearch/J/A+A/642/A58/tableg?http://vizieridia.saao.ac.za/viz-bin/conesearch/J/A+A/642/A58/tableg?GETtext/xml+votable180.050000true267.54992-18.699890.0055555555555555565/7175 7241 7319 6/28672-28673 28675-28676 28694-28695 28699 28720 28727 28736 28738 28836 28838-28839 28848 28850 28943 28954 28976 29270-29271 29448https://cdsarc.cds.unistra.fr/viz-bin/moc/J/A+A/642/A58?format=asciidefaultJ/A+A/642/A58/tablegCandidates to RRL sorted by probability of being an RRLSeq[1/242] Sequential numbermeta.recordintmagmeanMean of magnitudesmagphot.magdoublerecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintNameName (VVV JHHMMSS.ss+DDMMSS.s)meta.id;meta.maincharTileTile where the candidate is locatedobs.fieldcharRAJ2000Right ascension (J2000)degpos.eq.ra;meta.mainDEJ2000Declination (J2000)degpos.eq.dec;meta.mainPerCalculated perioddtime.period;meta.modelleddoubleAmpAmplitudemagphot.magdoubleProbProbability to be a RRLstat.probabilityfloatSimbadSimbad column added by the CDSmeta.ref.urlchar