AGN automatic photometric classification J/MNRAS/437/968 ivo://CDS.VizieR/J/MNRAS/437/968 doi:10.26093/cds/vizier.74370968 CDS Cavuoti S. Brescia M. D'Abrusco R. Longo G. Paolillo M. 2024-08-07T20:12:51Z 2013-12-12T15:23:49Z CDS support team CDS, Observatoire de Strasbourg, 11 rue de l'Universite, F-67000 Strasbourg, France cds-question@unistra.fr active-galactic-nuclei surveys galaxies catalogs visible-astronomy sloan-photometry In this paper, we discuss an application of machine-learning-based methods to the identification of candidate active galactic nucleus (AGN) from optical survey data and to the automatic classification ofAGNs in broad classes. We applied four different machine-learning algorithms, namely the Multi Layer Perceptron, trained, respectively, with the Conjugate Gradient, the Scaled Conjugate Gradient, the Quasi Newton learning rules and the Support Vector Machines, Q4 to tackle the problem of the classification of emission line galaxies in different classes, mainly AGNs versus non-AGNs, obtained using optical photometry in place of the diagnostics based on line intensity ratios which are classically used in the literature. Using the same photometric features, we discuss also the behaviour of the classifiers on finer AGN classification tasks, namely Seyfert I versus Seyfert II, and Seyfert versus LINER. Furthermore, we describe the algorithms employed, the samples of spectroscopically classified galaxies used to train the algorithms, the procedure followed to select the photometric parameters and the performances of our methods in terms of multiple statistical indicators. The results of the experiments show that the application of self-adaptive data mining algorithms trained on spectroscopic data sets and applied to carefully chosen photometric parameters represents a viable alternative to the classical methods that employ time-consuming spectroscopic observations. 2014MNRAS.437..968C https://cdsarc.cds.unistra.fr/viz-bin/cat/J/MNRAS/437/968 Catalog Research IsServedBy TAP VizieR generic service IsServedBy Conesearch service related-to II/267 : The SDSS Photometric Catalog, Release 4 (Adelman-McCarthy+, 2006) http://www.sdss.org : SDSS Home Page https://cds.unistra.fr/vizier-org/licences_vizier.html https://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/MNRAS/437/968 https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/MNRAS/437/968 http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/MNRAS/437/968 https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/MNRAS/437/968 https://vizier.iucaa.in/viz-bin/votable?-source=J/MNRAS/437/968 http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/MNRAS/437/968 GET text/xml+votable https://tapvizier.cds.unistra.fr/TAPVizieR/tap Cone search capability for table J/MNRAS/437/968/dame_agn (Candidate AGN objects from SDSS-DR4) https://vizier.cds.unistra.fr/viz-bin/conesearch/J/MNRAS/437/968/dame_agn? https://vizier.iucaa.in/viz-bin/conesearch/J/MNRAS/437/968/dame_agn? http://vizieridia.saao.ac.za/viz-bin/conesearch/J/MNRAS/437/968/dame_agn? 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Should Not be used for identification. meta.record int objID Unique SDSS identifier meta.id;meta.main RAJ2000 Right Ascension (J2000) deg pos.eq.ra;meta.main indexed DEJ2000 Declination (J2000) deg pos.eq.dec;meta.main indexed zph [0/0.3] Photometric redshift, computed from D'Abrusco et al., 2007ApJ...663..752D src.redshift double rmag r band flux in 3" diameter of the fiber radius mag phot.mag;em.opt.R float u-g u-g color index from deredenned SDSS magnitudes mag phot.color;em.opt.U;em.opt.B float g-r g-r color index from deredenned SDSS magnitudes mag phot.color;em.opt.B;em.opt.R float r-i r-i color index from deredenned SDSS magnitudes mag phot.color;em.opt.R;em.opt.I float i-z i-z color index from deredenned SDSS magnitudes mag phot.color;em.opt.I;em.opt.I float rmag0 Simplified r magnitude corrected for extinction mag phot.mag;em.opt.R float AGN [1/3] Quality Flag of the AGN classification: 1=basic, 2=medium, 3=high quality meta.code.qual int Sloan Display the SDSS data for this object meta.ref.url char DR4 Details from the SDSS-DR4 meta.ref.url char