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Cited by 68Galaxies: Formation, Evolution, PhenomenaAstrophysical Phenomena and ObservationsStellar, planetary, and galactic studies

Survey of Gravitationally-lensed Objects in HSC Imaging (SuGOHI): VI. Crowdsourced lens finding with Space Warps

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Sonnenfeld, Alessandro, Verma, Aprajita, More, Anupreeta, Baeten, Elisabeth, Macmillan, Christine, Wong, Kenneth C., Chan, James H. H., Jaelani, Anton T. +14 more·2020·Astronomy & Astrophysics
DOI 10.1051/0004-6361/202038067
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Abstract

Context.Strong lenses are extremely useful probes of the distribution of matter on galaxy and cluster scales at cosmological distances, however, they are rare and difficult to find. The number of currently known lenses is on the order of 1000. Aims.The aim of this study is to use crowdsourcing to carry out a lens search targeting massive galaxies selected from over 442 square degrees of photometric data from the Hyper Suprime-Cam (HSC) survey. Methods.Based on the S16A internal data release of the HSC survey, we chose a sample of ∼300 000 galaxies with photometric redshifts in the range of 0.2 < zphot < 1.2 and photometrically inferred stellar masses of log M* > 11.2. We crowdsourced lens finding on this sample of galaxies on the Zooniverse platform as part of the Space Warps project. The sample was complemented by a large set of simulated lenses and visually selected non-lenses for training purposes. Nearly 6000 citizen volunteers participated in the experiment. In parallel, we used Y ATTA L ENS, an automated lens-finding algorithm, to look for lenses in the same sample of galaxies. Results.Based on a statistical analysis of classification data from the volunteers, we selected a sample of the most promising ∼1500 candidates, which we then visually inspected: half of them turned out to be possible (grade C) lenses or better. By including lenses found by Y ATTA L ENS or serendipitously noticed in the discussion section of the Space Warps website, we were able to find 14 definite lenses (grade A), 129 probable lenses (grade B), and 581 possible lenses. Y ATTA L ENS found half the number of lenses that were discovered via crowdsourcing. Conclusions.Crowdsourcing is able to produce samples of lens candidates with high completeness, when multiple images are clearly detected, and with higher purity compared to the currently available automated algorithms. A hybrid approach, in which the visual inspection of samples of lens candidates pre-selected by discovery algorithms or coupled to machine learning is crowdsourced, will be a viable option for lens finding in the 2020s, with forthcoming wide-area surveys such as LSST,Euclid, and WFIRST.

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  • Even the largest citizen science lens search projects have only inspected up to around one million images.

    “Even the largest citizen science lens search projects have only inspected up to around one million images (Marshall et al. 2015; More et al. 2016; Sonnenfeld et al. 2020; González et al. 2025).”

    From Euclid Quick Data Release (Q1): XXVIII. The Strong Lensing Discovery Engine C – Finding lenses with machine learning (2026) · cites this paper as Sonnenfeld et al. 2020

    The Conclusions (page not provided) report that Space Warps–HSC, a citizen-science lens search, inspected a sample of about 300,000 galaxies—within the claimed million-image limit. The paper does not establish that this was among the largest projects or that citizen-science lens searches generally had inspected no more than around one million images. Its roughly 2.5 million classifications count repeated assessments, not distinct images. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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Authors
Sonnenfeld, A., Verma, A., More, A., Baeten, E., Macmillan, C., Wong, K. C., Chan, J. H. H., Jaelani, A. T., Lee, C.-H., Oguri, M., Rusu, C. E., Veldthuis, M., Trouille, L., Marshall, P. J., Hutchings, R., Allen, C., O’ Donnell, J., Cornen, C., Davis, C. P., McMaster, A., Lintott, C., & Miller, G.
Affiliations
  • Leiden University (NL)
  • University of Oxford (GB)
  • Kavli Institute for the Physics and Mathematics of the Universe (JP)
  • École Polytechnique Fédérale de Lausanne (CH)
  • Bandung Institute of Technology (ID)
  • National Astronomical Observatory of Japan (JP)
  • Adler Planetarium (US)
  • Kavli Institute for Particle Astrophysics and Cosmology (US)
ORCID
Venue
Astronomy & Astrophysics
Publisher
EDP Sciences
Published
2020-10
Volume
642
Pages
A148
ISSN
  • 0004-6361 (ISSN-L)
  • 1432-0746
DOI
10.1051/0004-6361/202038067
OpenAlex
W4288079647
Cited by
67
References
64
Citations by year
2026: 10 · 2025: 10 · 2024: 16 · 2023: 12 · 2022: 9 · 2021: 7 · 2020: 3
FWCI
3.45
Type
journal article
Language
en
Open access
Licence
www.edpsciences.org/en/authors/copyright-and-licensing (vor, 12 d)
Locations
  • Astronomy and Astrophysics (publishedVersion)
  • arXiv (Cornell University) (publishedVersion)
  • Leiden Repository (Leiden University) (submittedVersion)
  • Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
  • Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
  • arXiv (Cornell University)
Funders
Horizon 2020 (792916)
Topics
  • Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Galaxies: Formation, Evolution, Phenomena
  • Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Astrophysical Phenomena and Observations
  • Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Stellar, planetary, and galactic studies
Keywords
strong gravitational lensing, lens candidate selection, crowdsourcing, citizen science, Hyper Suprime-Cam, massive galaxies, photometric redshifts

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