J/MNRAS/456/2260ivo://CDS.VizieR/J/MNRAS/456/2260doi:10.26093/cds/vizier.74562260CDSArmstrong D.J.Kirk J.Lam K.W.F.McCormac J.Osborn H.P.Spake J.,Walker S.Brown D.J.A.Kristiansen M.H.Pollacco D.West R.,Wheatley P.J.2024-08-15T20:17:24Z2016-01-05T10:25:09ZCDS support team

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

cds-question@unistra.frvariable-starseclipsing-binary-starsWe are entering an era of unprecedented quantities of data from current and planned survey telescopes. To maximize the potential of such surveys, automated data analysis techniques are required. Here we implement a new methodology for variable star classification, through the combination of Kohonen Self-Organizing Maps (SOMs, an unsupervised machine learning algorithm) and the more common Random Forest (RF) supervised machine learning technique. We apply this method to data from the K2 mission fields 0-4, finding 154 ab-type RR Lyraes (10 newly discovered), 377 {delta} Scuti pulsators, 133 {gamma} Doradus pulsators, 183 detached eclipsing binaries, 290 semidetached or contact eclipsing binaries and 9399 other periodic (mostly spot-modulated) sources, once class significance cuts are taken into account. We present light-curve features for all K2 stellar targets, including their three strongest detected frequencies, which can be used to study stellar rotation periods where the observed variability arises from spot modulation. The resulting catalogue of variable stars, classes, and associated data features are made available online. We publish our SOM code in python as part of the open source pymvpa package, which in combination with already available RF modules can be easily used to recreate the method.2016MNRAS.456.2260Ahttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/MNRAS/456/2260CatalogResearchIsServedByTAP VizieR generic servicerelated-toJ/A+A/579/A19 : K2 Variable Catalogue (Armstrong+, 2015)http://deneb.astro.warwick.ac.uk/phrlbj/k2varcat/ : K2VarCat home pagehttps://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/MNRAS/456/2260https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/MNRAS/456/2260http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/MNRAS/456/2260https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/MNRAS/456/2260https://vizier.iucaa.in/viz-bin/votable?-source=J/MNRAS/456/2260http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/MNRAS/456/2260GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapdefaultJ/MNRAS/456/2260/table4Catalogue, Campaigns 0-4, Warwick lightcurvesIDEPIC IDmeta.id;meta.mainlongrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintCampaign[0/4] K2 Campaign when observedmeta.noteintClassVariability Class with Maximum Probabilitysrc.classcharPDSCUTDelta Scuti Class Probabilitystat.fit.goodnessdoublePEADetached Eclipsing Binary Class Probabilitystat.fit.goodnessdoublePEBNon-Detached Eclipsing Binary Class Probabilitystat.fit.goodnessdoublePGDORGamma Doradus Class Probabilitystat.fit.goodnessdoublePNoiseNoise Class Probabilitystat.fit.goodnessdoublePOTHPEROther Periodic Variable Class Probabilitystat.fit.goodnessdoublePRRabRR Lyrae ab Type Class Probabilitystat.fit.goodnessdoubleAnomalyAnomaly Score for Objectsrc.orbital.meanAnomalydoubleJ/MNRAS/456/2260/table5Data Features, Campaigns 0-4, Warwick lightcurvesrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintIDEPIC IDmeta.id;meta.mainlongCampaign[0/4] K2 Campaign when observedmeta.noteintSOMIndexLocation on Self-Organising-Mapmeta.idintPer1Dominant Lomb Scargle Perioddtime.perioddoublePer2Second Lomb Scargle Perioddtime.perioddoublePer3Third Lomb Scargle Perioddtime.perioddoubleSOMDistDistance to closest SOM pixelpos.angDistancedoublephp2pmeanMean point-to-point scatter in phasestat.variancedoublephp2pmaxMax point-to-point scatter in phasestat.variancedoubleAmplitudeMax-Min of phase curvesrc.var.amplitudedoubleAmpratio21Period 2 to period amplitude ratiophot.mag;arith.ratiodoubleAmpratio31Period 3 to period amplitude ratiophot.mag;arith.ratiodoublep2pmeanMean point-to-point scatterstat.variancedoublep2p98perc98th percentile of point-to-point scatterstat.variancedoublestdoverrFlux standard deviation over mean errorstat.variancedoubleJ/MNRAS/456/2260/table6Catalogue, Campaigns 3-4, PDC lightcurvesIDEPIC IDmeta.id;meta.mainlongrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintCampaign[0/4] K2 Campaign when observedmeta.noteintClassVariability Class with Maximum Probabilitysrc.classcharPDSCUTDelta Scuti Class Probabilitystat.fit.goodnessdoublePEADetached Eclipsing Binary Class Probabilitystat.fit.goodnessdoublePEBNon-Detached Eclipsing Binary Class Probabilitystat.fit.goodnessdoublePGDORGamma Doradus Class Probabilitystat.fit.goodnessdoublePNoiseNoise Class Probabilitystat.fit.goodnessdoublePOTHPEROther Periodic Variable Class Probabilitystat.fit.goodnessdoublePRRabRR Lyrae ab Type Class Probabilitystat.fit.goodnessdoubleAnomalyAnomaly Score for Objectsrc.orbital.meanAnomalydoubleJ/MNRAS/456/2260/table7Data Features, Campaigns 3-4, PDC lightcurvesrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintIDEPIC IDmeta.id;meta.mainlongCampaign[0/4] K2 Campaign when observedmeta.noteintSOMIndexLocation on Self-Organising-Mapmeta.idintPer1Dominant Lomb Scargle Perioddtime.perioddoublePer2Second Lomb Scargle Perioddtime.perioddoublePer3Third Lomb Scargle Perioddtime.perioddoubleSOMDistDistance to closest SOM pixelpos.angDistancedoublephp2pmeanMean point-to-point scatter in phasestat.variancedoublephp2pmaxMax point-to-point scatter in phasestat.variancedoubleAmplitudeMax-Min of phase curvesrc.var.amplitudedoubleAmpratio21Period 2 to period amplitude ratiophot.mag;arith.ratiodoubleAmpratio31Period 3 to period amplitude ratiophot.mag;arith.ratiodoublep2pmeanMean point-to-point scatterstat.variancedoublep2p98perc98th percentile of point-to-point scatterstat.variancedoublestdoverrFlux standard deviation over mean errorstat.variancedouble