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.
<Astron. Astrophys. 693, A291 (2025)>
=2025A&A...693A.291S 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 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 2013ApJ...777...97S, Cat. J/ApJ/777/97)
G14 = Gavazzi et al. (2014ApJ...785..144G 2014ApJ...785..144G, Cat. J/ApJ/785/144)
M16 = More et al. (2016MNRAS.455.1191M 2016MNRAS.455.1191M)
D17 = Diehl et al. (2017ApJS..232...15D 2017ApJS..232...15D, Cat. J/ApJS/232/15)
S18 = Sonnenfeld et al. (2018PASJ...70S..29S 2018PASJ...70S..29S)
W18 = Wong et al. (2018ApJ...867..107W 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 2019MNRAS.484.3879P, Cat. J/MNRAS/484/3879)
H20 = Huang et al. (2020ApJ...894...78H 2020ApJ...894...78H, Cat. J/ApJ/894/78)
J20 = Jaelani et al. (2020MNRAS.494.3156J 2020MNRAS.494.3156J)
Ch20 = Chan et al. (2020A&A...636A..87C 2020A&A...636A..87C)
Ca20 = Cao et al. (2020MNRAS.499.3610C 2020MNRAS.499.3610C, Cat. J/MNRAS/499/3610)
L20 = Li et al. (2020ApJ...899...30L 2020ApJ...899...30L)
S20 = Sonnenfeld et al. (2020A&A...642A.148S 2020A&A...642A.148S, Cat. J/A+A/642/A148)
C21 = Canameras et al. (2021A&A...653L...6C 2021A&A...653L...6C, Cat. J/A+A/653/L6)
T21 = Talbot et al. (2021MNRAS.502.4617T 2021MNRAS.502.4617T)
R22 = Rojas et al. (2022A&A...668A..73R 2022A&A...668A..73R, Cat. J/A+A/668/A73)
S22 = Shu et al. (2022A&A...667A.123S 2022A&A...667A.123S)
A23 = Andika et al. (2023A&A...678A.103A 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 2020A&A...644A.162S
Canameras et al. Paper II 2020A&A...644A.163C 2020A&A...644A.163C, Cat. J/A+A/644/A163
Huber et al. Paper III 2021A&A...646A.110H 2021A&A...646A.110H
Schuldt et al. Paper IV 2021A&A...646A.126S 2021A&A...646A.126S
Bayer et al. Paper V 2021A&A...653A..29B 2021A&A...653A..29B
Canameras et al. Paper VI 2021A&A...653L...6C 2021A&A...653L...6C, Cat. J/A+A/653/L6
Huber et al. Paper VII 2022A&A...658A.157H 2022A&A...658A.157H
Shu et al., Paper VIII 2022A&A...662A...4S 2022A&A...662A...4S, Cat. J/A+A/662/A4
Schuldt et al. Paper IX 2023A&A...671A.147S 2023A&A...671A.147S
Schuldt et al. Paper X 2023A&A...673A..33S 2023A&A...673A..33S
(End) Stefan Schuldt [UniMi, Italy], Patricia Vannier [CDS] 04-Nov-2024