J/A+A/693/A291 Characteristics of 162 strong lenses (Schuldt+, 2025) ================================================================================ HOLISMOKES. XIII: Strong-lens candidates at all mass scales and their environments from the Hyper-Suprime Cam and deep learning. Schuldt S., Canameras R., Andika I.T., Bag S., Melo A., Shu Y., Suyu S.H., Taubenberger S., Grillo C. =2025A&A...693A.291S (SIMBAD/NED BibCode) ================================================================================ ADC_Keywords: Clusters, galaxy ; Gravitational lensing ; Galaxies, photometry ; Photometry, CCD Keywords: gravitational lensing: strong - methods: data analysis - catalogs - galaxy: clusters: general Abstract: We 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. Description: Coordinates and characteristics of the high-confidence lens candidates identified with our ResNet (Table2) or by Canameras et al. (2021A&A...653L...6C, Cat. J/A+A/653/L6, Table3). All have scores p>=0.1, and average grades G>=1.5 from visual inspection. File Summary: -------------------------------------------------------------------------------- FileName Lrecl Records Explanations -------------------------------------------------------------------------------- ReadMe 80 . This file table2.dat 169 162 High-confidence lens candidates with ResNet scores p>=0.1, and average grades G>=1.5 from visual inspection table3.dat 185 384 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 -------------------------------------------------------------------------------- See also: J/A+A/644/A163 : Pan-STARRS lens candidates from neural networks (Canameras+ 2021) J/A+A/653/L6 : HSC-SSP lens candidates from neural networks (Canameras+, 2021) J/A+A/662/A4 : Strong-lens candidates from HSC-SSP PDR2 (Shu+, 2022) Byte-by-byte Description of file: table2.dat table3.dat -------------------------------------------------------------------------------- Bytes Format Units Label Explanations -------------------------------------------------------------------------------- 1- 14 A14 --- Name Name of strong lens candidate (HSCJHHMM+DDMM or HSCJHHMM+DDMMm) 16- 35 F20.16 deg RAdeg Right ascension (J2000.0) 37- 59 F23.19 deg DEdeg Declination (J2000.0) 61- 71 F11.9 --- p Network score 73- 77 F5.3 --- G Visual inspection grade 79- 97 F19.17 --- s_G Dispersion of G 99-118 F20.18 --- z ? Redshift 120-124 A5 --- OD-lit [false true] Overdensity based on literature 126-130 A5 --- OD-vis [false true] Overdensity based on visual inspection 132-136 A5 --- OD-z [false true] Overdensity based on photometric redshifts 138-139 I2 --- Nmax Absolute height of photo-z distribution 141-144 F4.2 --- zlow Photo-z lower bound 146-149 I4 --- Ntot Total number of photo-z in considered area 151-185 A35 --- Refs References (1) -------------------------------------------------------------------------------- Note (1): References of the previous discovery of the identified lens candidates are as follows: S13 = Sonnenfeld et al. (2013ApJ...777...97S, Cat. J/ApJ/777/97) G14 = Gavazzi et al. (2014ApJ...785..144G, Cat. J/ApJ/785/144) M16 = More et al. (2016MNRAS.455.1191M) D17 = Diehl et al. (2017ApJS..232...15D, Cat. J/ApJS/232/15) S18 = Sonnenfeld et al. (2018PASJ...70S..29S) W18 = Wong et al. (2018ApJ...867..107W, Cat. J/ApJ/867/107) H19 = Huang et al. (Confirming Strong Galaxy Gravitational Lenses in the DESI Legacy Imaging Surveys, HST Proposal. Cycle 27, ID. #15867) P19 = Petrillo et al. (2019MNRAS.484.3879P, Cat. J/MNRAS/484/3879) H20 = Huang et al. (2020ApJ...894...78H, Cat. J/ApJ/894/78) J20 = Jaelani et al. (2020MNRAS.494.3156J) Ch20 = Chan et al. (2020A&A...636A..87C) Ca20 = Cao et al. (2020MNRAS.499.3610C, Cat. J/MNRAS/499/3610) L20 = Li et al. (2020ApJ...899...30L) S20 = Sonnenfeld et al. (2020A&A...642A.148S, Cat. J/A+A/642/A148) C21 = Canameras et al. (2021A&A...653L...6C, Cat. J/A+A/653/L6) T21 = Talbot et al. (2021MNRAS.502.4617T) R22 = Rojas et al. (2022A&A...668A..73R, Cat. J/A+A/668/A73) S22 = Shu et al. (2022A&A...667A.123S) A23 = Andika et al. (2023A&A...678A.103A, Cat. J/A+A/678/A103) J23 = Jaelani et al. (arXiv e-prints, arXiv:2312.07333) ML = master lens catalog http://admin.masterlens.org -------------------------------------------------------------------------------- Acknowledgements: Stefan Schuldt, stefan.schuldt(at)unimi.it References: Suyu et al., Paper I 2020A&A...644A.162S Canameras et al. Paper II 2020A&A...644A.163C, Cat. J/A+A/644/A163 Huber et al. Paper III 2021A&A...646A.110H Schuldt et al. Paper IV 2021A&A...646A.126S Bayer et al. Paper V 2021A&A...653A..29B Canameras et al. Paper VI 2021A&A...653L...6C, Cat. J/A+A/653/L6 Huber et al. Paper VII 2022A&A...658A.157H Shu et al., Paper VIII 2022A&A...662A...4S, Cat. J/A+A/662/A4 Schuldt et al. Paper IX 2023A&A...671A.147S Schuldt et al. Paper X 2023A&A...673A..33S ================================================================================ (End) Stefan Schuldt [UniMi, Italy], Patricia Vannier [CDS] 04-Nov-2024