J/A+A/611/A53ivo://CDS.VizieR/J/A+A/611/A53doi:10.26093/cds/vizier.36110053CDSJamal S.Le Brun V.Le Fevre O.Vibert D.Schmitt A.Surace C.,Copin Y.Garilli B.Moresco M.Pozzetti L.2020-06-02T15:03:08Z2018-03-29T09:16:32ZCDS support team

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

cds-question@unistra.frgalaxiesspectroscopyredshiftedFuture large-scale surveys, as the ESA Euclid mission, will produce a large set of galaxy redshifts (>=10^6) that will require fully automated data-processing pipelines to analyze the data, extract crucial information and ensure that all requirements are met. A fundamental element in these pipelines is to associate to each galaxy redshift measurement a quality, or reliability, estimate. In this work, we introduce a new approach to automate the spectroscopic redshift reliability assessment based on machine learning (ML) and characteristics of the redshift probability density function. We propose to rephrase the spectroscopic redshift estimation into a Bayesian framework, in order to incorporate all sources of information and uncertainties related to the redshift estimation process and produce a redshift posterior probability density function (PDF). To automate the assessment of a reliability flag, we exploit key features in the redshift posterior PDF and machine learning algorithms.2018A&A...611A..53Jhttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/611/A53CatalogResearchIsServedByTAP VizieR generic serviceIsServedByConesearch servicerelated-toIII/250 : The VIMOS VLT deep survey (VVDS-DEEP) (Le Fevre+ 2005)https://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/A+A/611/A53https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/A+A/611/A53http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/A+A/611/A53https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/A+A/611/A53https://vizier.iucaa.in/viz-bin/votable?-source=J/A+A/611/A53http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/A+A/611/A53GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapCone search capability for table J/A+A/611/A53/table (Catalog of VVDS data and new zReliability labels)https://vizier.cds.unistra.fr/viz-bin/conesearch/J/A+A/611/A53/table?https://vizier.iucaa.in/viz-bin/conesearch/J/A+A/611/A53/table?http://vizieridia.saao.ac.za/viz-bin/conesearch/J/A+A/611/A53/table?GETtext/xml+votable180.050000true36.95895-4.818650.0055555555555555565/4759 6522 6812 6/17683 17686 17689 17692 19027 19030-19033 19035 26045 26083 26092 26094 26433 27256-27257 27259-27260 35935 359572.367228618189496e-19 2.6706780932288093e-19https://cdsarc.cds.unistra.fr/viz-bin/moc/J/A+A/611/A53?format=asciidefaultJ/A+A/611/A53/tableCatalog of VVDS data and new zReliability labelsrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintSurveySurvey (1)meta.notecharFieldVVDS field (VVDS-FNNNN-NN or VVDS-CDFS)meta.id;obs.fieldcharObsIDObservation identificationmeta.id;obs.param;obslongRAJ2000Right ascension (J2000)degpos.eq.ra;meta.mainindexedDEJ2000Declination (J2000)degpos.eq.dec;meta.mainindexedImagAB magnitude in I filtermagphot.mag;em.opt.IdoublezspSpectroscopic redshiftsrc.redshiftdoubleq_zsp[1/9] Initial VVDS z quality flags (2)meta.code.qualintRelzsp[C1 C2 C3 C4 C5] New VVDS z reliability flags (3)meta.code.qualcharIAUNameName based on J2000 position (VVDS-JHHMMSS.ss+DDMMSS.s)meta.id;meta.maincharVVDS-DEEPDisplay the VIMOS VLT deep survey data (Le Febvre, 2005, Cat. III/250)meta.ref.urlcharSimbadSimbad column added by the CDSmeta.ref.urlchar