J/MNRAS/489/663ivo://CDS.VizieR/J/MNRAS/489/663doi:10.26093/cds/vizier.74890663CDSBrescia M.Salvato M.Cavuoti S.Ananna T.T.Riccio G.Lamassa S.M.,Urry C.M.Longo G.2024-08-20T20:13:25Z2023-02-09T13:36:14ZCDS support team

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

cds-question@unistra.fractive-galactic-nucleix-ray-sourcesredshiftedastrometryvisible-astronomyWith the launch of eROSITA (extended Roentgen Survey with an Imaging Telescope Array), successfully occurred on 2019 July 13, we are facing the challenge of computing reliable photometric redshifts for 3 million of active galactic nuclei (AGNs) over the entire sky, having available only patchy and inhomogeneous ancillary data. While we have a good understanding of the photo-z quality obtainable for AGN using spectral energy distribution (SED)-fitting technique, we tested the capability of machine learning (ML), usually reliable in computing photo-z for QSO in wide and shallow areas with rich spectroscopic samples. Using MLPQNA as example of ML, we computed photo-z for the X-ray-selected sources in Stripe 82X, using the publicly available photometric and spectroscopic catalogues. Stripe 82X is at least as deep as eROSITA will be and wide enough to include also rare and bright AGNs. In addition, the availability of ancillary data mimics what can be available in the whole sky. We found that when optical, and near- and mid-infrared data are available, ML and SED fitting perform comparably well in terms of overall accuracy, realistic redshift probability density functions, and fraction of outliers, although they are not the same for the two methods. The results could further improve if the photometry available is accurate and including morphological information. Assuming that we can gather sufficient spectroscopy to build a representative training sample, with the current photometry coverage we can obtain reliable photo-z for a large fraction of sources in the Southern hemisphere well before the spectroscopic follow-up, thus timely enabling the eROSITA science return. The photo-z catalogue is released here.2019MNRAS.489..663Bhttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/MNRAS/489/663CatalogResearchIsServedByTAP VizieR generic serviceIsServedByConesearch servicerelated-toJ/ApJ/817/172 : X-ray Observations of Stripe 82 (LaMassa+, 2016)J/ApJ/850/66 : Stripe 82X survey multiwavelength catalog (Ananna+, 2017)https://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/MNRAS/489/663https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/MNRAS/489/663http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/MNRAS/489/663https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/MNRAS/489/663https://vizier.iucaa.in/viz-bin/votable?-source=J/MNRAS/489/663http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/MNRAS/489/663GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapCone search capability for table J/MNRAS/489/663/tablea1 (Example of contents of the photo-z catalogue made publicly available)https://vizier.cds.unistra.fr/viz-bin/conesearch/J/MNRAS/489/663/tablea1?https://vizier.iucaa.in/viz-bin/conesearch/J/MNRAS/489/663/tablea1?http://vizieridia.saao.ac.za/viz-bin/conesearch/J/MNRAS/489/663/tablea1?GETtext/xml+votable180.050000true0.9801910.20460450.0055555555555555565/4438 4457-4458 4500-4502 4505 4522 4697 5802 6/17406-17407 17662-17663 17742-17743 17745 17748-17749 17758 17761 17763-17764 17766-17767 17769 17772-17773 17780 17806-17807 17816-17817 17819 17826-17827 17836 17840-17842 17991 17997 17999 18012 18019 18024-18025 18028 18032 18065 18069 18071 18073-18074 18076 18078 18085-18086 18092 18096 18112 18114 18176 18766 18772-18774 18777-18780 18782 18785 18787 18793-18796 18800 18865 18881 19023 19027 19030 19032-19033 19035-19036 19038 19045 19056 19058 19091 19097 19099 19110 19456-19458 23102 23145 23148 23150 23182-23183 23190-23192 23196-23197 23234 30029 30031 36861 36863https://cdsarc.cds.unistra.fr/viz-bin/moc/J/MNRAS/489/663?format=asciiX-rayOpticaldefaultJ/MNRAS/489/663/tablea1Example of contents of the photo-z catalogue made publicly availablerecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintID[1/196076] Object identifier from Ananna et al. (2017ApJ...850...66A, Cat. J/ApJ/850/66)meta.id;meta.mainintRAJ2000Right ascension (J2000)degpos.eq.ra;meta.mainDEJ2000Declination (J2000)degpos.eq.dec;meta.mainzsdssVWI? Photometric redshift obtained by combining SDSS, VHS, IRAC and WISE surveyssrc.redshift.photdoublenullablezsdssVW? Photometric redshift obtained by combining SDSS, VHS and WISE surveyssrc.redshift.photdoublenullablezsdssVI? Photometric redshift obtained by combining SDSS, VHS and IRAC surveyssrc.redshift.photdoublenullablezsdssW? Photometric redshift obtained by combining SDSS and WISEsrc.redshift.photdoublenullablezsdssI? Photometric redshift obtained by combining SDSS and IRACsrc.redshift.photdoublenullablezsdssV? Photometric redshift obtained by combining SDSS and VHSsrc.redshift.photdoublenullablezsdss? Photometric redshift from SDSSsrc.redshift.photdoublenullablezMLPQNA? Best photometric redshift obtained from all the previous combinationssrc.redshift.photdoublenullable