J/MNRAS/484/5330ivo://CDS.VizieR/J/MNRAS/484/5330doi:10.26093/cds/vizier.74845330CDSJacobs C.Collett T.Glazebrook K.McCarthy C.Qin A.K.Abbott T.M.C.,Abdalla F.B.Annis J.Avila S.Bechtol K.Bertin E.Brooks D.,Buckley-Geer E.Burke D.L.Carnero Rosell A.Carrasco Kind M.,Carretero J.Da Costa L.N.Davis C.De Vicente J.Desai S.Diehl H.T.,Doel P.Eifler T.F.Flaugher B.Frieman J.Garcia-Bellido J.,Gaztanaga E.Gerdes D.W.Goldstein D.A.Gruen D.Gruendl R.A.,Gschwend J.Gutierrez G.Hartley W.G.Hollowood D.L.Honscheid K.,Hoyle B.James D.J.Kuehn K.Kuropatkin N.Lahav O.Li T.S.Lima M.,Lin H.Maia M.A.G.Martini P.Miller C.J.Miquel R.Nord B.,Plazas A.A.Sanchez E.Scarpine V.Schubnell M.Serrano S.,Sevilla-Noarbe I.Smith M.Soares-Santos M.Sobreira F.Suchyta E.,Swanson M.E.C.Tarle G.Vikram V.Walker A.R.Zhang Y.Zuntz J.,(The DES Collaboration)2024-08-19T20:16:56Z2022-06-10T09:17:04ZCDS support team

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

cds-question@unistra.frgravitational-lensinginfrared-photometryvisible-astronomybroad-band-photometryredshiftedWe search Dark Energy Survey (DES) Year 3 imaging data for galaxy-galaxy strong gravitational lenses using convolutional neural networks. We generate 250000 simulated lenses at redshifts>0.8 from which we create a data set for training the neural networks with realistic seeing, sky and shot noise. Using the simulations as a guide, we build a catalogue of 1.1 million DES sources with 1.8<g-i<5, 0.6<g-r<3, rmag>19, g_mag>20, and imag>18.2. We train two ensembles of neural networks on training sets consisting of simulated lenses, simulated non-lenses, and real sources. We use the neural networks to score images of each of the sources in our catalogue with a value from 0 to 1, and select those with scores greater than a chosen threshold for visual inspection, resulting in a candidate set of 7301 galaxies. During visual inspection, we rate 84 as "probably" or "definitely" lenses. Four of these are previously known lenses or lens candidates. We inspect a further 9428 candidates with a different score threshold, and identify four new candidates. We present 84 new strong lens candidates, selected after a few hours of visual inspection by astronomers. This catalogue contains a comparable number of high-redshift lenses to that predicted by simulations. Based on simulations, we estimate our sample to contain most discoverable lenses in this imaging and at this redshift range.2019MNRAS.484.5330Jhttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/MNRAS/484/5330CatalogResearchIsServedByTAP VizieR generic serviceIsServedByConesearch servicerelated-toII/357 : The Dark Energy Survey (DES): Data Release 1 (Abbott+, 2018)II/371 : The Dark Energy Survey (DES): Data Release 2 (Abott+, 2021)J/ApJ/690/1236 : COSMOS photometric redshift catalog (Ilbert+, 2009)J/MNRAS/413/813 : ATLAS3D project. I. (Cappellari+, 2011)J/ApJ/749/38 : CFHTLS-SL2S-ARCS strong lens candidates (More+, 2012)J/ApJ/785/144 : SL2S galaxy-scale sample of lens candidates (Gavazzi+, 2014)J/MNRAS/465/4914 : R-band light curves of HE 0435-1223 (Bonvin+, 2017)J/ApJS/232/15 : Candidate strong lens systems from DES obs. (Diehl+, 2017)J/ApJS/243/17 : Strong DES lens cand. from neural networks (Jacobs+, 2019)J/ApJS/259/27 : DES Bright Arcs Survey: strong lens syst. (O'Donnell+, 2022)https://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/MNRAS/484/5330https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/MNRAS/484/5330http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/MNRAS/484/5330https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/MNRAS/484/5330https://vizier.iucaa.in/viz-bin/votable?-source=J/MNRAS/484/5330http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/MNRAS/484/5330GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapCone search capability for table J/MNRAS/484/5330/table4 (New candidates from visual inspection of the neural network-selected sources)https://vizier.cds.unistra.fr/viz-bin/conesearch/J/MNRAS/484/5330/table4?https://vizier.iucaa.in/viz-bin/conesearch/J/MNRAS/484/5330/table4?http://vizieridia.saao.ac.za/viz-bin/conesearch/J/MNRAS/484/5330/table4?GETtext/xml+votable180.050000true0.8183-33.80120.0055555555555555566/16433 16449 16471 16673 16733 17439 17444 17485 17517 17642 17666 17795 17961 18774 18800 20670 20674 20712 21051 21081 33238 33272 33402 33654 33788 33807 33864 33883 33906 33923 33996 34076 34167 34393 34419 34433 34504 34795 34811 35016 35043 35164 35228 35489 35620 35766 35862 35867 35908 35910 35963 36000 36052 36122 36168 36173 36184 36188 36258 36304 36334 36389 36393 36396 36488 36525 36615 36712 39463 39558 39591 39595 45485 45681 45962 46135 46209 46241 46285 46347 46476 46503 46661 46663https://cdsarc.cds.unistra.fr/viz-bin/moc/J/MNRAS/484/5330?format=asciiOpticalInfrareddefaultJ/MNRAS/484/5330/table4New candidates from visual inspection of the neural network-selected sourcesrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintDESSystem candidate designation (JHHMM+DDMM)meta.id;meta.maincharObjID[66052645/507569548] Object identifier from DESmeta.idlongRAJ2000Right ascension in decimal degrees (J2000)degpos.eq.ra;meta.mainDEJ2000[-65.1/0.7] Declination in decimal degrees (J2000)degpos.eq.dec;meta.mainGrade[1/3] Grade (3=most certainly containing a lens) (1)meta.code.qualfloatimag[18.69/22.57] i-band magnitudemagphot.mag;em.opt.Ifloatzphot[0.43/0.86] Photometric redshiftsrc.redshiftfloate_zphot[0.28/0.48] zphot uncertaintystat.errorfloat