J/A+A/653/L6 HSC-SSP lens candidates from neural networks (Canameras+, 2021)
HOLISMOKES. VI.
New galaxy-scale strong lens candidates from the HSC-SSP imaging survey.
Canameras 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.
<Astron. Astrophys. 653, L6 (2021)>
=2021A&A...653L...6C 2021A&A...653L...6C (SIMBAD/NED BibCode)
ADC_Keywords: Gravitational lensing; Surveys; Redshifts; Photometry, ugriz
Keywords: gravitational lensing: strong - methods: data analysis
Abstract:
We 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.
Description:
Complete list of galaxy-scale strong lens candidates with lens
luminous red galaxies (LRGs) from a systematic search in DR2 of the
HSC Wide survey. These systems were selected with a deep residual
network, as high confidence candidates with network scores >0.1 and
average grades >1.5 from visual inspection (i.e. corresponding to
definite or probable lenses). For each candidate, the neural network
score, average visual grade, photometry, and photometric or
spectroscopic redshift are given.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
table1.dat 94 467 Complete list of strong lens candidates from visual
inspection of the highest neural network scores
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See also:
J/A+A/644/A163 : Pan-STARRS lens candidates from neural networks
(Canameras+ 2020)
Byte-by-byte Description of file: table1.dat
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Bytes Format Units Label Explanations
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1- 13 A13 --- Name Candidate identifier (HSCJHHMM+DDMM)
16- 23 F8.4 deg RAdeg Right Ascension (J2000)
25- 33 F9.5 deg DEdeg Declination (J2000)
36- 39 F4.2 --- pNet Output score from the neural network
42- 44 F3.1 --- Gavg Average of the visual grades from five authors
47- 49 F3.1 --- Gdisp Dispersion in the grades
52 I1 --- Gthree Number of classifiers assigning a grade of 3
55- 59 F5.2 mag gKron HSC DR2 catalog g band Kron magnitude (1)
62- 66 F5.2 mag rKron HSC DR2 catalog r band Kron magnitude (1)
69- 73 F5.2 mag iKron HSC DR2 catalog i band Kron magnitude (1)
76- 81 F6.4 --- z Redshift (2)
84 A1 --- f_z [*] * for spectroscopic redshift,
else photometric
87 A1 --- Gflag [+] SuGOHI grade C flag (3)
90- 94 A5 --- Ref Reference(s) for candidates in
the literature (4)
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Note (1): Kron magnitudes of the lens and source blends.
Note (2): Photometric redshifts from neural networks published in
Schuldt et al. (2021A&A...651A..55S 2021A&A...651A..55S, Cat. J/A+A/651/A55),
with spectroscopic redshifts substituted for sources marked with f_z.
Note (3): Flag for candidates having a lower grade C in SuGOHI.
Note (4): References for sources previously published as confirmed or
candidate lens systems (grades A and B or equivalent) as follows:
a = Sonnenfeld et al. (2018PASJ...70S..29S 2018PASJ...70S..29S)
b = Wong et al. (2018ApJ...867..107W 2018ApJ...867..107W)
c = Chan et al. (2020A&A...636A..87C 2020A&A...636A..87C)
d = Sonnenfeld et al. (2020A&A...642A.148S 2020A&A...642A.148S, Cat. J/A+A/642/A148)
e = Jaelani et al. (2020MNRAS.495.1291J 2020MNRAS.495.1291J)
f = Canameras et al. (2020A&A...644A.163C 2020A&A...644A.163C, Cat. J/A+A/644/A163)
g = Huang et al. (2020ApJ...894...78H 2020ApJ...894...78H)
h = Stark et al. (2013MNRAS.436.1040S 2013MNRAS.436.1040S)
i = Jacobs et al. (2019ApJS..243...17J 2019ApJS..243...17J, Cat. J/ApJS/243/17)
j = Li et al. (2020ApJ...899...30L 2020ApJ...899...30L)
k = More et al. (2012ApJ...749...38M 2012ApJ...749...38M, Cat. J/ApJ/749/38)
l = Petrillo et al. (2019MNRAS.484.3879P 2019MNRAS.484.3879P)
m = Brownstein et al. (2012ApJ...744...41B 2012ApJ...744...41B)
n = Gavazzi et al. (2014ApJ...785..144G 2014ApJ...785..144G, Cat. J/ApJ/785/144)
o = Diehl et al. (2017ApJS..232...15D 2017ApJS..232...15D, Cat. J/ApJS/232/15)
p = More et al. (2016MNRAS.455.1191M 2016MNRAS.455.1191M)
q = Shu et al. (2016ApJ...824...86S 2016ApJ...824...86S, Cat. J/ApJ/824/86)
r = Jacobs et al. (2017MNRAS.471..167J 2017MNRAS.471..167J)
s = Ratnatunga et al. (1995ApJ...453L...5R 1995ApJ...453L...5R)
t = Tanaka et al. (2016ApJ...826L..19T 2016ApJ...826L..19T)
u = More et al. (2017MNRAS.465.2411M 2017MNRAS.465.2411M)
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Acknowledgements:
Raoul Canameras, rcanameras(at)mpa-garching.mpg.de
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
(End) Raoul Canameras [MPA, Germany], Patricia Vannier [CDS] 09-Sep-2021