Space Warps– II. New gravitational lens candidates from the CFHTLS discovered through citizen science
Open accessAbstract
We report the discovery of 29 promising (and 59 total) new lens candidates from the Canada–France–Hawaii Telescope Legacy Survey (CFHTLS) based on about 11 million classifications performed by citizen scientists as part of the first Space Warps lens search. The goal of the blind lens search was to identify lens candidates missed by robots (the ringfinder on galaxy scales and arcfinder on group/cluster scales) which had been previously used to mine the CFHTLS for lenses. We compare some properties of the samples detected by these algorithms to the Space Warps sample and find them to be broadly similar. The image separation distribution calculated from the Space Warps sample shows that previous constraints on the average density profile of lens galaxies are robust. Space Warps recovers about 65 per cent of known lenses, while the new candidates show a richer variety compared to those found by the two robots. This detection rate could be increased to 80 per cent by only using classifications performed by expert volunteers (albeit at the cost of a lower purity), indicating that the training and performance calibration of the citizen scientists is very important for the success of Space Warps. In this work we present the SIMCT pipeline, used for generating in situ a sample of realistic simulated lensed images. This training sample, along with the false positives identified during the search, has a legacy value for testing future lens-finding algorithms. We make the pipeline and the training set publicly available.
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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 More et al. 2016
Section 2.3 reports about 430,000 image cut-outs for the SPACE WARPS search, supporting that this project inspected fewer than one million images. The paper does not establish an upper limit for the largest citizen-science lens searches generally; its roughly 11 million classifications are repeat assessments, not distinct images. Page numbers are unavailable in our copy. (LLM, conf=0.82)
Experimental — accuracy not yet measuredPrevious lens-finding citizen-science projects have been able to search through a larger number of images (e.g. 430 000 in the CFHT Legacy Survey SW project).
“Previous lens-finding citizenscience projects have been able to search through a larger number of images (e.g. 430 000 in the CFHT Legacy Survey SW project; see More et al. 2016)”
From Euclid Quick Data Release (Q1): XXVIII. The Strong Lensing Discovery Engine C – Finding lenses with machine learning (2026) · cites this paper as More et al. 2016
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Research works printed but not linked: 2
- Marshall P. et al., 2015, preprint (arXiv:1504.06148)(PaperI) · arXiv
- Thanjavur K. G., 2009, PhD thesis, Univ. Victoria, Canada
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Self-citation
- Authors
- More, A., Verma, A., Marshall, P. J., More, S., Baeten, E., Wilcox, J., Macmillan, C., Cornen, C., Kapadia, A., Parrish, M., Snyder, C., Davis, C. P., Gavazzi, R., Lintott, C. J., Simpson, R., Miller, D., Smith, A. M., Paget, E., Saha, P., Küng, R., & Collett, T. E.
- Affiliations
- Kavli Institute for the Physics and Mathematics of the Universe (JP)
- University of Oxford (GB)
- Adler Planetarium (US)
- Kavli Institute for Particle Astrophysics and Cosmology (US)
- Centre National de la Recherche Scientifique (FR)
- University of Zurich (CH)
- University of Portsmouth (GB)
- Corresponding author
- More, Anupreeta
- Verma, Aprajita
- Marshall, Philip J.
- ORCID
- Venue
- Monthly Notices of the Royal Astronomical Society
- Publisher
- Oxford University Press (OUP)
- Published
- 2015-11-10
- Volume
- 455
- Issue
- 2
- Pages
- 1191-1210
- ISSN
- 0035-8711 (ISSN-L)
- 1365-2966
- OpenAlex
- W1793679032
- Cited by
- 116
- References
- 60
- Citations by year
- 2026: 6 · 2025: 12 · 2024: 17 · 2023: 5 · 2022: 13 · 2021: 10 · 2020: 11 · 2019: 11 · 2018: 9 · 2017: 14 · 2016: 7 · 2015: 1
- FWCI
- 5.88
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Monthly Notices of the Royal Astronomical Society (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, other-oa)
- arXiv (Cornell University) (submittedVersion)
- reroDoc Digital Library (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- HAL (submittedVersion, other-oa)
- HighWire Press Open Archive (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- National Science Foundation (DE-AC02-76SF00515, 1066293)
- U.S. Department of Energy (76SF00515, DE–AC02–76SF00515, AC02‐76SF00515, DE-AC02-)
- Aspen Center for Physics (1066293, PHYS-1066293)
- Leverhulme Trust
- Ministry of Education, Culture, Sports, Science and Technology
- Office of Science (AC02-76SF00515, DE-AC02-76SF0051 5, DE-AC02, 76SF00515)
- Japan Society for the Promotion of Science (DE-AC02-76SF00515)
- Basic Energy Sciences (DE-AC02, 76SF00515, AC02-76SF00515, DE-AC-02-76SF00515)
- SLAC National Accelerator Laboratory (DEAC02−76SF00515, AC02‐76SF00515)
- 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 › Cosmology and Gravitation Theories
- Keywords
- gravitational lensing, citizen science, CFHTLS, false positives
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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