J/A+A/475/1159ivo://CDS.VizieR/J/A+A/475/1159doi:10.26093/cds/vizier.34751159CDSDebosscher J.Sarro L.M.Aerts C.Cuypers J.Vandenbussche B.,Garrido R.Solano E.2017-10-16T14:58:08Z2008-04-19T06:02:07ZCDS support team

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

cds-question@unistra.frvariable-starsThe fast classification of new variable stars is an important step in making them available for further research. Selection of science targets from large databases is much more efficient if they have been classified first. Defining the classes in terms of physical parameters is also important to get an unbiased statistical view on the variability mechanisms and the borders of instability strips. Our goal is twofold: provide an overview of the stellar variability classes that are presently known, in terms of some relevant stellar parameters; use the class descriptions obtained as the basis for an automated `supervised classification' of large databases. Such automated classification will compare and assign new objects to a set of pre-defined variability training classes. For every variability class, a literature search was performed to find as many well-known member stars as possible, or a considerable subset if too many were present. Next, we searched on-line and private databases for their light curves in the visible band and performed period analysis and harmonic fitting. The derived light curve parameters are used to describe the classes and define the training classifiers. We compared the performance of different classifiers in terms of percentage of correct identification, of confusion among classes and of computation time. We describe how well the classes can be separated using the proposed set of parameters and how future improvements can be made, based on new large databases such as the light curves to be assembled by the CoRoT and Kepler space missions. The derived classifiers' performances are so good in terms of success rate and computational speed that we will evaluate them in practice from the application of our methodology to a large subset of variable stars in the OGLE database and from comparison of the results with published OGLE variable star classifications based on human intervention. These results will be published in a subsequent paper.2007A&A...475.1159Dhttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/475/1159CatalogResearchIsServedByTAP VizieR generic serviceIsServedByConesearch servicehttps://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/A+A/475/1159https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/A+A/475/1159http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/A+A/475/1159https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/A+A/475/1159https://vizier.iucaa.in/viz-bin/votable?-source=J/A+A/475/1159http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/A+A/475/1159GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapCone search capability for table J/A+A/475/1159/list (List of stars 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Should Not be used for identification.meta.recordintNameStar namemeta.id;meta.maincharNlcNumber of lightcurve solutionsmeta.numberintnullableRAJ2000? Simbad right ascension (J2000)pos.eq.ra;meta.mainnullableDEJ2000? Simbad Declination (J2000)pos.eq.dec;meta.mainnullablevarVariability code, in codes.dat filemeta.code;src.varcharvar2Second variability code, in codes.dat filemeta.code;src.varcharFileNameName of the file containing the lightcurvemeta.id;meta.filecharFileName3Name of the file containing the lightcurvemeta.id;meta.filecharFileName4Name of the file containing the lightcurvemeta.id;meta.filecharSimbadSimbad column added by the CDScharJ/A+A/475/1159/defattsLight curve parametersrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintFileNameName of the file containing the lightcurve (1)meta.id;meta.filecharNameSimbad column added by the CDSmeta.idcharf11th frequencyd**-1stat.fit.paramdoublef22th frequencyd**-1stat.fit.paramdoublef33th frequencyd**-1stat.fit.paramdoubleamp111th harmonic amplitude of f1magsrc.var.amplitudefloatamp122th harmonic amplitude of f1magsrc.var.amplitudefloatamp133th harmonic amplitude of f1magsrc.var.amplitudefloatamp144th harmonic amplitude of f1magsrc.var.amplitudefloatamp211th harmonic amplitude of f2magsrc.var.amplitudefloatamp222th harmonic amplitude of f2magsrc.var.amplitudefloatamp233th harmonic amplitude of f2magsrc.var.amplitudefloatamp244th harmonic amplitude of f2magsrc.var.amplitudefloatamp311th harmonic amplitude of f3magsrc.var.amplitudefloatamp322th harmonic amplitude of f3magsrc.var.amplitudefloatamp333th harmonic amplitude of f3magsrc.var.amplitudefloatamp344th harmonic amplitude of f3magsrc.var.amplitudefloatphi12phase of amp12, if phase of amp11=0radtime.phasefloatphi13phase of amp13, if phase of amp11=0radtime.phasefloatphi14phase of amp14, if phase of amp11=0radtime.phasefloatphi21phase of amp21, if phase of amp11=0radtime.phasefloatphi22phase of amp22, if phase of amp11=0radtime.phasefloatphi23phase of amp23, if phase of amp11=0radtime.phasefloatphi24phase of amp24, if phase of amp11=0radtime.phasefloatphi31phase of amp31, if phase of amp11=0radtime.phasefloatphi32phase of amp32, if phase of amp11=0radtime.phasefloatphi33phase of amp33, if phase of amp11=0radtime.phasefloatphi34phase of amp34, if phase of amp11=0radtime.phasefloattrendSlope of linear trendmag/dstat.fit.paramfloatvarratVariance ratio (2)stat.variancefloatvarredVariance reduction (3)stat.variancefloatvarVariability code, in codes.dat filemeta.code;src.varcharJ/A+A/475/1159/codesClassification codesrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintCodeCodemeta.id;meta.maincharClassDefinition of the classmeta.code;src.varcharNclThe total number of light curves present in the dataset for the classmeta.numberint