Classifying galaxy spectra at 0.5<z<1J/MNRAS/478/4416ivo://CDS.VizieR/J/MNRAS/478/4416doi:10.26093/cds/vizier.74784416CDSRahmani S.Teimoorinia H.Barmby P.2024-08-18T20:20:10Z2021-12-10T13:58:30ZCDS support teamCDS, Observatoire de Strasbourg, 11 rue de l'Universite, F-67000 Strasbourg, Francecds-question@unistra.frgalaxiesspectroscopyThe spectrum of a galaxy contains information about its physical properties. Classifying spectra using templates helps to elucidate the nature of a galaxy's energy sources. In this paper, we investigate the use of self-organizing maps in classifying galaxy spectra against templates. We trained semi-supervised self-organizing map networks using a set of templates covering the wavelength range from far ultraviolet to near-infrared. The trained networks were used to classify the spectra of a sample of 142 galaxies with 0.5<z<1 and the results compared to classifications performed using K-means clustering, a supervised neural network, and chi-squared minimization. Spectra corresponding to quiescent galaxies were more likely to be classified similarly by all methods while starburst spectra showed more variability. Compared to classification using chi-squared minimization or the supervised neural network, the galaxies classed together by the self-organizing map had more similar spectra. The class ordering provided by the 1D self-organizing maps corresponds to an ordering in physical properties, a potentially important feature for the exploration of large data sets.2018MNRAS.478.4416Rhttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/MNRAS/478/4416CatalogResearchIsServedByTAP VizieR generic serviceIsServedByConesearch servicehttps://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/MNRAS/478/4416https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/MNRAS/478/4416http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/MNRAS/478/4416https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/MNRAS/478/4416https://vizier.iucaa.in/viz-bin/votable?-source=J/MNRAS/478/4416http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/MNRAS/478/4416GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapCone search capability for table J/MNRAS/478/4416/tablea1 (Properties of best-fitting models created by Teimoorinia (2012AJ....144..172T))https://vizier.cds.unistra.fr/viz-bin/conesearch/J/MNRAS/478/4416/tablea1?https://vizier.iucaa.in/viz-bin/conesearch/J/MNRAS/478/4416/tablea1?http://vizieridia.saao.ac.za/viz-bin/conesearch/J/MNRAS/478/4416/tablea1?GETtext/xml+votable180.050000true53.0607917-27.82038890.0055555555555555566/35935https://cdsarc.cds.unistra.fr/viz-bin/moc/J/MNRAS/478/4416?format=asciidefault
J/MNRAS/478/4416/tablea1Properties of best-fitting models created by Teimoorinia (2012AJ....144..172T)SimbadNameSimbad column added by the CDSmeta.idcharnullablezRedshiftsrc.redshiftfloatrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintspShow the spectrummeta.ref.urlcharIDInternal galaxy identifier (G1-G142)meta.idcharGOODSGOODS name (GDS-JHHMMS.ss+DDMMSS.s)meta.id;meta.maincharlogM*Stellar masslog(solMass)phys.massfloate_logM*rms uncertainty on logM*log(solMass)stat.errorfloatAgeAgeGyrtime.agefloate_Agerms uncertainty on AgeGyrstat.error;time.agefloatlogsSFRSpecific star formation ratelog(Gyr**-1)meta.idfloate_logsSFRrms uncertainty on logsSFRlog(Gyr**-1)stat.errorfloatlog(fburst)Mass fraction of young single population (SP) modelphys.mass;meta.modelledfloate_log(fburst)rms uncertainty on log(frac-burst)stat.errorfloatA(FUV)Attenuation at 1500{AA}magphys.absorptionfloate_A(FUV)rms uncertainty on A(FUV)magstat.errorfloatT12ClassTeimoorinia (2012AJ....144..172T) classificationsrc.classchar_RAPositions from name (right ascension part)degpos.eq.ra;meta.main_DEPositions from name (declination part)degpos.eq.dec;meta.main