J/A+A/692/A260ivo://CDS.VizieR/J/A+A/692/A260doi:10.26093/cds/vizier.36920260CDSRoster W.Salvato M.Krippendorf S.Saxena A.Shirley R.Buchner J.,Wolf J.Dwelly T.Bauer F.E.Aird J.Ricci C.Assef R.J.,Anderson S.F.Liu X.Merloni A.Weller J.Nandra K.2025-02-26T20:02:49Z2024-12-18T08:25:29ZCDS support team

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

cds-question@unistra.fractive-galactic-nucleiredshiftedvisible-astronomyx-ray-sourcesComputing reliable photometric redshifts (photo-z) for active galactic nuclei (AGN) is a challenging task, primarily due to the complex interplay between the unresolved relative emissions associated with the supermassive black hole and its host galaxy. Spectral energy distribution (SED) fitting methods, while effective for galaxies and AGN in pencil-beam surveys, face limitations in wide or all-sky surveys with fewer bands available, lacking the ability to accurately capture the AGN contribution to the SED, hindering reliable redshift estimation. This limitation is affecting the many 10s of millions of AGN detected in existing datasets, e.g., those AGN clearly singled out and identified by SRG/eROSITA. Our goal is to enhance photometric redshift performance for AGN in all-sky surveys while simultaneously simplifying the approach by avoiding the need to merge multiple data sets. Instead, we employ readily available data products from the 10th Data Release of the Imaging Legacy Survey for the Dark Energy Spectroscopic Instrument, which covers >20000deg^2^ of extragalactic sky with deep imaging and catalog-based photometry in the grizW1-W4 bands. We fully utilize the spatial flux distribution in the vicinity of each source to produce reliable photo-z. We introduce PICZL, a machine-learning algorithm leveraging an ensemble of convolutional neural networks. Utilizing a cross-channel approach, the algorithm integrates distinct SED features from images with those obtained from catalog-level data. Full probability distributions are achieved via the integration of Gaussian mixture models. On a validation sample of 8098 AGN, PICZL achieves an accuracy {sigma}NMAD of 4.5% with an outlier fraction {eta} of 5.6%. These results significantly outperform previous attempts to compute accurate photo-z for AGN using machine learning. We highlight that the model's performance depends on many variables, predominantly the depth of the data and associated photometric error. A thorough evaluation of these dependencies is presented in the paper. Our streamlined methodology maintains consistent performance across the entire survey area, when accounting for differing data quality. The same approach can be adopted for future deep photometric surveys such as LSST and Euclid, showcasing its potential for wide-scale realization. With this paper, we release an updated photo-z (including errors) for the XMM-SERVS W-CDF-S, ELAIS-S1 and LSS fields.2024A&A...692A.260Rhttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/692/A260CatalogResearchIsServedByTAP VizieR generic serviceIsServedByConesearch servicerelated-toVII/292 : DESI Legacy Imaging Surveys DR8 photometric redshiftsJ/MNRAS/478/2132 : XMM-LSS field. New XMM-Newton point-source cat.J/ApJS/256/21 : XMM-SERVS survey: X-ray sources for W-CDF-S + ELAIS-S1https://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/A+A/692/A260https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/A+A/692/A260http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/A+A/692/A260https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/A+A/692/A260https://vizier.iucaa.in/viz-bin/votable?-source=J/A+A/692/A260http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/A+A/692/A260GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapCone search capability for table J/A+A/692/A260/photo-z (Photo-z catalog)https://vizier.cds.unistra.fr/viz-bin/conesearch/J/A+A/692/A260/photo-z?https://vizier.iucaa.in/viz-bin/conesearch/J/A+A/692/A260/photo-z?http://vizieridia.saao.ac.za/viz-bin/conesearch/J/A+A/692/A260/photo-z?GETtext/xml+votable180.050000true34.2083115276803-5.29509273847270.0055555555555555565/4422 8847 8983 8989 6/17677 17679 17681-17683 17686 17692-17694 17712-17713 35382-35383 35385 35387 35473 35476 35931 35953 36104 36106 36128https://cdsarc.cds.unistra.fr/viz-bin/moc/J/A+A/692/A260?format=asciiX-rayOpticaldefaultJ/A+A/692/A260/photo-zPhoto-z catalogrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintXIDUnique source ID assigned to each X-ray source in the original papers (XID) (1)meta.id;meta.maincharSurvey[ES1 LSS WCD] Original survey where the source was detected (SURVEY) (2)meta.notecharLS10Unique LS source ID (DESI Legacy Survey, LS DR10) assigned to optical counterpart (LS10_FULLID) (3)meta.idcharRAJ2000Right Ascension (J2000) of the LS10 optical counterpart (LS10_RA)degpos.eq.ra;meta.mainDEJ2000Declination (J2000) of the LS10 optical counterpart (LS10_DEC)degpos.eq.dec;meta.mainzsp? Spectroscopic redshift from original catalog (SPECZ)src.redshiftdoublenullablezphPhotometric redshift from PICZL (PHZ_PICZL)src.redshift.photfloatb1zphPICZL Photometric redshift minimum at 1 sigma (PHZ_PICZL_l68)src.redshift.photfloatB1zphPICZL Photometric redshift maximum at 1 sigma (PHZ_PICZL_u68)src.redshift.photfloatb3zphPICZL Photometric redshift minimum at 3 sigma (PHZ_PICZL_l99)src.redshift.photfloatB3zphPICZL Photometric redshift maximum at 3 sigma (PHZ_PICZL_u99)src.redshift.photfloat