J/A+A/705/A247ivo://CDS.VizieR/J/A+A/705/A247CDSArevalo P.Sanchez-Saez P.Sotomayor B.Lira P.Bauer F.E.Rios S.2026-03-02T06:25:50Z2026-01-23T19:41:03ZCDS support team

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

cds-question@unistra.fractive-galactic-nucleivisible-astronomyWe explored the potential of optical variability selection methods in order to identify a broad range of active galactic nuclei (AGN), including those challenging to detect with conventional techniques. Using the unprecedented combination of depth, sky coverage, and cadence of the Zwicky Transient Facility (ZTF) survey, we specifically target low-luminosity, low-mass, and starlight-dominated AGN, known for their redder colours, weaker variability signals, and difficult nuclear photometry due to their resolved host galaxies. We performed aperture photometry on ZTF reference-subtracted g-band images for ~39.8 million sources across >8000deg^2^; assembled light curves and calculated features for all detected sources; and classified objects employing a random forest algorithm into 14 distinct classes, including 341 938 candidate AGN across four classes (low-z, mid-z, high-z, and blazars). We compared the variability metrics derived from our photometry to those obtained from publicly available ZTF data release light curves, obtained through psf-photometry on the science, i.e. not reference-subtracted images (DR11-psf), to assess the impact of our analysis. Finally, we compared our AGN candidate sample with those identified through colour selection and X-ray detection techniques. We find that the fraction of low-z quiescent galaxies exhibiting significant variability drops dramatically (from 98% of the sample to 7% of the sample, when using the standard variability metric Pvar) when replacing the DR11-psf light curves with our difference image, aperture photometry (DI-Ap) version. The overall number of variable low-z AGN remains high (99% when using DR11-psf light curves, 83% when using DI-Ap); however, it implies that our photometry can detect the fainter variability in host dominated AGN. The classifier effectively distinguishes between AGN and other sources, demonstrating high recovery rates even for AGN in resolved nearby galaxies. AGN candidates in eROSITA's eFEDS field, detected in X-rays and bright enough for ZTF optical observations, were classified as AGN (79%) and non-variable galaxies (20%). These groups show a 2dex difference in X-ray luminosity, but not in X-ray flux. A significant fraction of X-ray AGN are optically too faint for ZTF, and conversely, one-quarter of ZTF AGN in the eFEDS area lack X-ray detections, highlighting a wide range of X-ray-to-optical flux ratios in the AGN population.2026A&A...705A.247Ahttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/705/A247CatalogResearchIsServedByTAP VizieR generic serviceIsServedByConesearch servicehttps://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/A+A/705/A247https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/A+A/705/A247http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/A+A/705/A247https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/A+A/705/A247https://vizier.iucaa.in/viz-bin/votable?-source=J/A+A/705/A247http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/A+A/705/A247GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapCone search capability for table J/A+A/705/A247/catalog (Coordinates, some light curve features, 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Should Not be used for identification.meta.recordintOIDObject identifier from this workmeta.id;meta.maincharRAJ2000Right ascension (J2000)degpos.eq.ra;meta.mainindexedDEJ2000Declination (J2000)degpos.eq.dec;meta.mainindexedmagMeanAverage magnitudemagphot.magfloatnEpochsNumber of good epochs (catflags=0)meta.numberintTimeRangeTotal length of light curve useddtime.intervalfloatindexedPvarProbability that the source is intrinsically variablestat.fit.goodnessdoubleStdStandard deviation of the light curvemagstat.stdev;phot.magfloatGPDRWtauRelaxation time from DRW modelingdtime.epochdoublepredInitClassPredicted class from node_initmeta.code.classcharpredInitClassProb? Predicted class probability from node_initmeta.code.classfloatnullablepredVarClassPredicted class from node_variablemeta.code.classcharpredVarClassProb? Predicted class probability from node_initmeta.code.classfloatnullablepredClassFinal predicted classmeta.code.classcharpredClassProbFinal predicted class probabilitystat.fit.goodnessfloatCleanAgnCand[0/1] Flag, 1: in clean AGN catalog, 0: not in clean AGN catalogmeta.codeint