J/MNRAS/507/5034ivo://CDS.VizieR/J/MNRAS/507/5034doi:10.26093/cds/vizier.75075034CDSRazim O.Cavuoti S.Brescia M.Riccio G.Salvato M.Longo G.2024-08-21T20:18:36Z2021-10-28T09:11:41ZCDS support team

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

cds-question@unistra.frastronomical-modelsredshiftedgalaxiescatalogsIn order to answer the open questions of modern cosmology and galaxy evolution theory, robust algorithms for calculating photometric redshifts (photo-z) for very large samples of galaxies are needed. Correct estimation of the various photo-z algorithms' performance requires attention to both the performance metrics and the data used for the estimation. In this work, we use the supervised machine learning algorithm MLPQNA (Multi-Layer Perceptron with Quasi-Newton Algorithm) to calculate photometric redshifts for the galaxies in the COSMOS2015 catalogue and the unsupervised Self-Organizing Maps (SOM) to determine the reliability of the resulting estimates. We find that for z_spec_<1.2, MLPQNA photo-z predictions are on the same level of quality as spectral energy distribution fitting photo-z. We show that the SOM successfully detects unreliable zspec that cause biases in the estimation of the photo-z algorithms' performance. Additionally, we use SOM to select the objects with reliable photo-z predictions. Our cleaning procedures allow us to extract the subset of objects for which the quality of the final photo-z catalogues is improved by a factor of 2, compared to the overall statistics.2021MNRAS.507.5034Rhttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/MNRAS/507/5034CatalogResearchIsServedByTAP VizieR generic serviceIsServedByConesearch servicerelated-toJ/ApJS/224/24 : The COSMOS2015 catalog (Laigle+, 2016)https://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/MNRAS/507/5034https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/MNRAS/507/5034http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/MNRAS/507/5034https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/MNRAS/507/5034https://vizier.iucaa.in/viz-bin/votable?-source=J/MNRAS/507/5034http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/MNRAS/507/5034GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapCone search capability for table J/MNRAS/507/5034/mlphotoz (COSMOS2015 machine learning photometric redshifts with reliability indicators derived with SOM)https://vizier.cds.unistra.fr/viz-bin/conesearch/J/MNRAS/507/5034/mlphotoz?https://vizier.iucaa.in/viz-bin/conesearch/J/MNRAS/507/5034/mlphotoz?http://vizieridia.saao.ac.za/viz-bin/conesearch/J/MNRAS/507/5034/mlphotoz?GETtext/xml+votable180.050000true149.932956321.618003270.0055555555555555565/6814 6/27245 27247 27344https://cdsarc.cds.unistra.fr/viz-bin/moc/J/MNRAS/507/5034?format=asciidefaultJ/MNRAS/507/5034/mlphotozCOSMOS2015 machine learning photometric redshifts with reliability indicators derived with SOMrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintRAJ2000[149.41/150.79] Right ascension (J2000)degpos.eq.ra;meta.mainindexedDEJ2000[1.61/2.82] Declination (J2000)degpos.eq.dec;meta.mainindexedSeqObject ID in the original COSMOS2015 catalog, Laigle et al., 2016, Cat. J/ApJS/224/24)meta.id;meta.mainintdataset[Run Test Train] A flag indicating whether the object was included in the train, test or run samples during MLPQNA trainingmeta.codecharzphMl[0.02/1.47] Photometric redshift obtained with MLPQNA (photoZ_ML)src.redshift.phot;meta.modelleddoublezphMlCoeff? In-cell outlier coefficient for ML photo-z (photoZ_ML_outlCoeff) (1)stat.fit.paramdoublenullablezphSED[0.0/4.72] SED fitting photometric redshift derived from the COSMOS2015 (photoZ_SED)src.redshift.phot;meta.modelleddoublezphSEDCoeff? In-cell outlier coefficient for SED fitting photo-z (photoZ_SED_outlCoeff) (1)stat.fit.paramdoublenullableresML-SED[-1.11/0.75] Residuals between ML and SED fitting photo-z calculated as resid=(z_SED-z_ML)/(1+z_SED) (residML_SED)stat.fit.residual;stat.fit;meta.modelleddoublezspCoeff? In-cell outlier coefficient for spec-z (specZ_outlCoeff) (1)stat.fit.paramdoublenullabletMOOccupation of the SOM cell, to which this object belongs, by the train dataset (trainMapOccupation)stat.fit.paramfloat