J/MNRAS/353/211ivo://CDS.VizieR/J/MNRAS/353/211doi:10.26093/cds/vizier.73530211CDSCarballo R.Cofino A.S.Gonzalez-serrano J.I.2024-06-29T20:15:20Z2006-02-25T14:49:29ZCDS support team

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

cds-question@unistra.frquasarsThe application of supervised artificial neural networks (ANNs) for quasar selection from combined radio and optical surveys with photometric and morphological data is investigated, using the list of candidates and their classification from the work of White et al. (2000, Cat. J/ApJS/126/133>) Seven input parameters and one output, evaluated to 1 for quasars and 0 for non-quasars during the training, were used, with architectures 7: 1 and 7: 2: 1. Both models were trained on samples of 800 sources and yielded similar performance on independent test samples, with reliability as large as 87 per cent at 80 per cent completeness (or 90 to 80 per cent for completeness from 70 to 90 per cent). For comparison, the quasar fraction from the original candidate list was 56 per cent.2004MNRAS.353..211Chttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/MNRAS/353/211CatalogResearchIsServedByTAP VizieR generic serviceIsServedByConesearch servicerelated-toJ/ApJS/126/133 : The FIRST bright quasar survey. II. (White+, 2000)https://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/MNRAS/353/211https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/MNRAS/353/211http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/MNRAS/353/211https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/MNRAS/353/211https://vizier.iucaa.in/viz-bin/votable?-source=J/MNRAS/353/211http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/MNRAS/353/211GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapCone search capability for table J/MNRAS/353/211/table4 (Quasar probabilities for the 98 candidates without spectroscopic classification in White et al. (2000, Cat. <J/ApJS/126/133>))https://vizier.cds.unistra.fr/viz-bin/conesearch/J/MNRAS/353/211/table4?https://vizier.iucaa.in/viz-bin/conesearch/J/MNRAS/353/211/table4?http://vizieridia.saao.ac.za/viz-bin/conesearch/J/MNRAS/353/211/table4?GETtext/xml+votable180.050000true108.772534.0836110.0055555555555555566/4542 4591 4816 4984 5092 5113 5133 5237 5239 5244 5273 5312 5327 5440 5443 5477-5478 5481 5547 5798 6151 6156 6266 6349 6361 6370-6371 6512 8656 8661 8679 8687 8831 8910 8983 9012 9063 9094 9119 9159 9169 9178 9252 9269 9431 9760 9785 9836 9859 9875-9876 9884 9899 9905 10269 10279 10284 10287 10315 10326 10565 10779 10795 10860 10880 10883 10891 10903 10919 28089 28095 28107-28108 28110 28120 28122-28123 28126 28435 28469 28506 28516 28530 28540 28552 28559 28570 28573 28599 28601 28607-28608 28637 28657 32506https://cdsarc.cds.unistra.fr/viz-bin/moc/J/MNRAS/353/211?format=asciiOpticaldefaultJ/MNRAS/353/211/table4Quasar probabilities for the 98 candidates without spectroscopic classification in White et al. (2000, Cat. <J/ApJS/126/133>)recnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintFIRSTJFIRST J designation (based on J2000.0 position)charP7-1Quasar probability from ANN 7:1 method (1)floate_P7-1rms uncertainty on P7-1floatP7-2-1Quasar probability from ANN 7:2:1 method (1)floate_P7-2-1rms uncertainty on P7-2-1floatPOC1Quasar probability from oblique decision tree classifier OC1 (Murthy et al., 1994, J. Artif. Intell. Res., 2, 1)float_RAName (derived from FIRSTJ position) (right ascension part)degpos.eq.ra;meta.main_DEName (derived from FIRSTJ position) (declination part)degpos.eq.dec;meta.main