J/A+A/693/A291ivo://CDS.VizieR/J/A+A/693/A291doi:10.26093/cds/vizier.36930291CDSSchuldt S.Canameras R.Andika I.T.Bag S.Melo A.Shu Y.Suyu S.H.,Taubenberger S.Grillo C.2025-03-03T20:01:32Z2025-01-28T10:36:52ZCDS support team

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

cds-question@unistra.frgalaxy-clustersgravitational-lensinggalaxiesphotometryccd-photometryWe have performed a systematic search for strong gravitational lenses using Hyper Suprime-Cam (HSC) imaging data, focusing on galaxy-scale lenses combined with an environment analysis resulting in the identification of lensing clusters. To identify these lens candidates, we exploit our residual neural network from HOLISMOKES VI, which is trained on realistic gri mock-images as positive examples, and real HSC images as negative examples. Compared to our previous work, where we have successfully applied the classifier to around 62.5 million galaxies having i-Kron radius >=0.8", we now lower the i-Kron radius limit to >=0.5". This results in an increase by around 73 million sources to more than 135 million images. During our visual multi-stage grading of the network candidates, we now also inspect simultaneously larger stamps (80"x80") to identify large, extended arcs cropped in the 10"x10" cutouts and classify additionally their overall environment. Here, we also re-inspect our previous lens candidates with i-Kron radii >=0.8" and classify their environment. Using the 546 visually identified lens candidates, we further define various criteria by exploiting extensive and complementary photometric redshift catalogs, to select the candidates in overdensities. In total, we identified 24 grade-A and 138 grade-B candidates with either spatially-resolved multiple images or extended, distorted arcs in the new sample. Furthermore, combining our different techniques to determine overdensities, we identify in total 231/546 lens candidates by at least one of our three identification methods of overdensities, containing only 49 group- or cluster-scale re-discoveries, while 43 systems are identified by all three procedures. We further perform a statistical analysis by using the neural network from HOLISMOKES IX to model these systems as singular isothermal ellipsoids with external shear and to estimate their parameter values, making this the largest uniformly modeled sample. We find a tendency towards larger Einstein radii for galaxy-scale systems in overdense environments, while the other parameter values as well as uncertainty distributions are consistent between those in overdense and non-overdense environments. These results demonstrate the feasibility of downloading and applying neural network classifiers to hundreds of million cutouts, necessary in the upcoming era of big data from deep, wide-field imaging surveys like Euclid and the Rubin Observatory Legacy Survey of Space and Time, while leading to a sample size that can be visually inspected by humans. These deep learning pipelines, with false-positive rates of ~0.01%, are very powerful tools to identify such rare galaxy-scale strong lensing systems, while also aiding in the discovery of new strong lensing clusters.2025A&A...693A.291Shttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/693/A291CatalogResearchIsServedByTAP VizieR generic serviceIsServedByConesearch servicerelated-toJ/A+A/644/A163 : Pan-STARRS lens candidates from neural networksJ/A+A/653/L6 : HSC-SSP lens candidates from neural networksJ/A+A/662/A4 : Strong-lens candidates from HSC-SSP PDR2 (Shu+, 2022)https://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/A+A/693/A291https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/A+A/693/A291http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/A+A/693/A291https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/A+A/693/A291https://vizier.iucaa.in/viz-bin/votable?-source=J/A+A/693/A291http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/A+A/693/A291GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapCone search capability for table J/A+A/693/A291/table2 (High-confidence lens candidates with ResNet scores p>=0.1, and average grades G>=1.5 from visual inspection)https://vizier.cds.unistra.fr/viz-bin/conesearch/J/A+A/693/A291/table2?https://vizier.iucaa.in/viz-bin/conesearch/J/A+A/693/A291/table2?http://vizieridia.saao.ac.za/viz-bin/conesearch/J/A+A/693/A291/table2?GETtext/xml+votable180.050000true352.8770160.6259390.005555555555555556Cone search capability for table J/A+A/693/A291/table3 (High-confidence lens candidates reported by Canameras et al. (2021, Cat. J/A+A/653/L6) and not listed in Table 2, now with analysis of their environment)https://vizier.cds.unistra.fr/viz-bin/conesearch/J/A+A/693/A291/table3?https://vizier.iucaa.in/viz-bin/conesearch/J/A+A/693/A291/table3?http://vizieridia.saao.ac.za/viz-bin/conesearch/J/A+A/693/A291/table3?GETtext/xml+votable180.050000true151.2157724403412-0.52915113107070.0055555555555555565/1027 2459-2460 2652 5460 6490 6500 6568 6739 6826-6827 6/4098 4100 4102-4104 4112 4115-4117 4119-4120 8192 8195 8200 9646 9822 9831 9833 9848 9877 9879 9884-9885 9887 9899 9901 9903-9905 9907-9908 9913 9923 9985-9987 10583 10587 10589 10591 10606 10689 10692 11267 11272 11424-11425 17527 17675 17677-17678 17681 17684-17685 17689-17691 17693-17694 17696 17698-17699 17702-17706 17710-17712 17714-17718 17720 17722 17724-17725 17728 17730-17731 17736-17737 17739 17742 17760 17793 17798-17799 17808-17809 18006 18008 18010-18011 18033 18035 18040 18089 18092-18094 18176 18687 18749-18751 18759 18774 18776-18777 18782 18785 18787 18789-18793 18795-18796 18798 18801 18804 18808 18810 18837-18838 18844 18850 18854 18856-18857 18860 18862 18869-18870 18877-18878 18880 18883 18885 18888 18890-18891 19025 19028 19031-19032 19035 19038 19053 19059-19060 19156 19200-19202 19204 19206-19207 19210-19211 19213-19214 19217-19218 19221 19224 19226 19229 19236 19238 19240 19245 19264 19458 19460 19462 19464 21829 21831 21837 21844-21845 21847-21850 21852 21855 21872-21873 21876 21878 25595-25598 25911 25915 25918-25919 25931 25934-25935 25938 25940 25942 25944-25946 25949 25953 25955 25957-25959 25964 25966 25968-25969 25976 25989 25991 25996 25998-25999 26004 26006 26008 26012 26020 26022 26029 26032 26034 26036 26050-26051 26056 26171 26173 26175 26218 26222 26253 26255 26258-26259 26261-26262 26267 26279-26280 26282-26283 26285 26288-26289 26291-26292 26304 26951 26953 26962-26963 26965 26967-26969 26971-26972 26981 26983 26994 27194 27196 27213-27214 27219 27225 27235 27237 27240-27241 27245 27247-27248 27251 27256-27257 27259 27271 27277-27281 27285 27288-27290 27296 27298 27303 27312 27315-27316 27321 27324 27326-27327 27330 27333 27335 27345 27648 27651 27656 31402 36796 36799 36842 40953 40955 40957-40959 45049 45052https://cdsarc.cds.unistra.fr/viz-bin/moc/J/A+A/693/A291?format=asciidefaultJ/A+A/693/A291/table2High-confidence lens candidates with ResNet scores p>=0.1, and average grades G>=1.5 from visual inspectionNtotTotal number of photo-z in considered areameta.idintRefsReferences (1)meta.ref;pos.framecharRAJ2000Right ascension (J2000.0)degpos.eq.ra;meta.mainDEJ2000Declination (J2000.0)degpos.eq.dec;meta.mainpNetwork scorestat.fit.goodnessdoubleGVisual inspection grademeta.code.qualfloats_GDispersion of Ginstr.dispersiondoublez? Redshiftsrc.redshiftdoublenullableOD-lit[false true] Overdensity based on literaturemeta.codecharOD-vis[false true] Overdensity based on visual inspectionmeta.codecharOD-z[false true] Overdensity based on photometric redshiftsmeta.codecharNmaxAbsolute height of photo-z distributionmeta.numberintzlowPhoto-z lower boundsrc.redshift.photfloatrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintNameName of strong lens candidate (HSCJHHMM+DDMM or HSCJHHMM+DDMMm)meta.id;meta.maincharJ/A+A/693/A291/table3High-confidence lens candidates reported by Canameras et al. (2021, Cat. J/A+A/653/L6) and not listed in Table 2, now with analysis of their environmentz? Redshiftsrc.redshiftdoublenullablezlowPhoto-z lower boundsrc.redshift.photfloatNtotTotal number of photo-z in considered areameta.idintRefsReferences (1)meta.ref;pos.framecharrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintNameName of strong lens candidate (HSCJHHMM+DDMM or HSCJHHMM+DDMMm)meta.id;meta.maincharRAJ2000Right ascension (J2000.0)degpos.eq.ra;meta.mainDEJ2000Declination (J2000.0)degpos.eq.dec;meta.mainpNetwork scorestat.fit.goodnessdoubleGVisual inspection grademeta.code.qualfloats_GDispersion of Ginstr.dispersiondoubleOD-lit[false true] Overdensity based on literaturemeta.codecharOD-vis[false true] Overdensity based on visual inspectionmeta.codecharOD-z[false true] Overdensity based on photometric redshiftsmeta.codecharNmaxAbsolute height of photo-z distributionmeta.numberint