J/A+A/653/L6ivo://CDS.VizieR/J/A+A/653/L6doi:10.26093/cds/vizier.36539006CDSCanameras R.Schuldt S.Shu Y.Suyu S.H.Taubenberger S.Meinhardt T.,Leal-Taixe L.Chao D.C.-Y.Inoue K.T.Jaelani A.T.More A.2021-12-03T12:43:40Z2021-09-17T11:58:32ZCDS support team

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

cds-question@unistra.frgravitational-lensingsurveysredshiftedinfrared-photometryvisible-astronomybroad-band-photometryWe have carried out a systematic search for galaxy-scale strong lenses in multiband imaging from the Hyper Suprime-Cam (HSC) survey. Our automated pipeline, based on realistic strong-lens simulations, deep neural network classification, and visual inspection, is aimed at efficiently selecting systems with wide image separations (Einstein radii ~1.0-3.0"), intermediate redshift lenses (z~0.4-0.7), and bright arcs for galaxy evolution and cosmology. We classified gri images of all 62.5 million galaxies in HSC Wide with i-band Kron radius >0.8" to avoid strict preselections and to prepare for the upcoming era of deep, wide-scale imaging surveys with Euclid and Rubin Observatory. We obtained 206 newly-discovered candidates classified as definite or probable lenses with either spatially-resolved multiple images or extended, distorted arcs. In addition, we found 88 high-quality candidates that were assigned lower confidence in previous HSC searches, and we recovered 173 known systems in the literature. These results demonstrate that, aided by limited human input, deep learning pipelines with false positive rates as low as ~0.01% can be very powerful tools for identifying the rare strong lenses from large catalogs, and can also largely extend the samples found by traditional algorithms. We provide a ranked list of candidates for future spectroscopic confirmation.2021A&A...653L...6Chttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/653/L6CatalogResearchIsServedByTAP VizieR generic serviceIsServedByConesearch servicerelated-toJ/A+A/644/A163 : Pan-STARRS lens candidates from neural networkshttps://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/A+A/653/L6https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/A+A/653/L6http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/A+A/653/L6https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/A+A/653/L6https://vizier.iucaa.in/viz-bin/votable?-source=J/A+A/653/L6http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/A+A/653/L6GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapCone search capability for table J/A+A/653/L6/table1 (Complete list of strong lens candidates from visual inspection of the highest neural network scores)https://vizier.cds.unistra.fr/viz-bin/conesearch/J/A+A/653/L6/table1?https://vizier.iucaa.in/viz-bin/conesearch/J/A+A/653/L6/table1?http://vizieridia.saao.ac.za/viz-bin/conesearch/J/A+A/653/L6/table1?GETtext/xml+votable180.050000true15.65971.982470.0055555555555555565/2459-2460 2652 6500 6/4098 4100 4102 4104 4109 4111-4112 4115-4117 4119-4120 8192 8195 8200 9646 9822 9831 9833 9877 9884-9885 9887 9899 9901 9903-9905 9907 9913 9986 10583 10587 10589 10606 10689 10692 11272 11424 17527 17675 17677-17678 17681 17684-17685 17689-17691 17693-17694 17696 17698-17699 17702-17706 17710-17712 17714-17718 17720 17722 17724 17728 17730-17731 17736-17737 17739 17742 17760 17793 17798-17799 17808-17810 18006 18008 18010 18033 18035 18040 18089 18092-18094 18176 18687 18749-18751 18759 18772 18774 18776-18777 18782 18787 18789-18793 18795-18796 18798 18801 18808 18810 18837-18838 18844 18854 18856 18860 18862 18869-18870 18877-18878 18883 18888 18890-18891 19031-19032 19035 19038 19053 19059-19060 19156 19200-19202 19204 19206-19207 19210 19213-19214 19217 19221 19224 19226 19229 19236 19238 19240 19245 19264 19458 19460 19462 21829 21837 21840 21842-21845 21847-21850 21852 21855 21872-21873 21876 21878 25595-25598 25911 25915 25918 25931 25934-25935 25940 25942 25944-25946 25949 25953 25955 25957-25961 25963-25964 25966 25968-25969 25976 25989 25991 25996 25998-25999 26004 26006 26008 26012 26020 26022 26029 26032 26034 26036 26050-26051 26056 26170 26173 26175 26218 26222 26253 26255 26258-26259 26261 26267 26272 26274-26275 26280 26282-26283 26285 26288-26289 26291-26292 26304 26951 26953 26956-26957 26959 26962-26963 26965 26967-26969 26972 26981 26983 26994 27194 27196 27213-27214 27219 27225 27235 27237 27240-27241 27245 27247 27251 27256-27257 27271 27277-27281 27285 27288-27290 27296 27298 27303-27305 27307-27310 27312 27315-27316 27321 27324 27326-27327 27330 27335 27345 27648 27651 27656 31402 36796 36799 36842 40955 40957-40959 45049https://cdsarc.cds.unistra.fr/viz-bin/moc/J/A+A/653/L6?format=asciiOpticalInfrareddefaultJ/A+A/653/L6/table1Complete list of strong lens candidates from visual inspection of the highest neural network scoresrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintNameCandidate identifier (HSCJHHMM+DDMM)meta.id;meta.maincharRAJ2000Right Ascension (J2000)degpos.eq.ra;meta.mainDEJ2000Declination (J2000)degpos.eq.dec;meta.mainpNetOutput score from the neural networkstat.fit.goodnessfloatGavgAverage of the visual grades from five authorsmeta.code.qualfloatGdispDispersion in the gradesmeta.code.qualfloatGthreeNumber of classifiers assigning a grade of 3meta.code.qualintgKronHSC DR2 catalog g band Kron magnitude (1)magphot.mag;em.opt.BfloatrKronHSC DR2 catalog r band Kron magnitude (1)magphot.mag;em.opt.RfloatiKronHSC DR2 catalog i band Kron magnitude (1)magphot.mag;em.opt.IfloatzRedshift (2)src.redshiftfloatf_z[*] * for spectroscopic redshift, else photometricmeta.codecharGflag[+] SuGOHI grade C flag (3)meta.codecharRefReference(s) for candidates in the literature (4)meta.ref;pos.framechar