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Cited by 152Galaxies: Formation, Evolution, PhenomenaAstrophysical Phenomena and ObservationsStellar, planetary, and galactic studies

The strong gravitational lens finding challenge

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Metcalf, R. B., Meneghetti, M., Avestruz, C., Bellagamba, F., Bom, C. R., Bertin, E., Cabanac, R., Courbin, F. +29 more·2019·Astronomy & Astrophysics
DOI 10.1051/0004-6361/201832797
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Abstract

Large-scale imaging surveys will increase the number of galaxy-scale strong lensing candidates by maybe three orders of magnitudes beyond the number known today. Finding these rare objects will require picking them out of at least tens of millions of images, and deriving scientific results from them will require quantifying the efficiency and bias of any search method. To achieve these objectives automated methods must be developed. Because gravitational lenses are rare objects, reducing false positives will be particularly important. We present a description and results of an open gravitational lens finding challenge. Participants were asked to classify 100 000 candidate objects as to whether they were gravitational lenses or not with the goal of developing better automated methods for finding lenses in large data sets. A variety of methods were used including visual inspection, arc and ring finders, support vector machines (SVM) and convolutional neural networks (CNN). We find that many of the methods will be easily fast enough to analyse the anticipated data flow. In test data, several methods are able to identify upwards of half the lenses after applying some thresholds on the lens characteristics such as lensed image brightness, size or contrast with the lens galaxy without making a single false-positive identification. This is significantly better than direct inspection by humans was able to do. Having multi-band, ground based data is found to be better for this purpose than single-band space based data with lower noise and higher resolution, suggesting that multi-colour data is crucial. Multi-band space based data will be superior to ground based data. The most difficult challenge for a lens finder is differentiating between rare, irregular and ring-like face-on galaxies and true gravitational lenses. The degree to which the efficiency and biases of lens finders can be quantified largely depends on the realism of the simulated data on which the finders are trained.

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Authors
Metcalf, R. B., Meneghetti, M., Avestruz, C., Bellagamba, F., Bom, C. R., Bertin, E., Cabanac, R., Courbin, F., Davies, A., Decencière, E., Flamary, R., Gavazzi, R., Geiger, M., Hartley, P., Huertas-Company, M., Jackson, N., Jacobs, C., Jullo, E., Kneib, J.-P., Koopmans, L. V. E., Lanusse, F., Li, C.-L., Ma, Q., Makler, M., Li, N., Lightman, M., Petrillo, C. E., Serjeant, S., Schäfer, C., Sonnenfeld, A., Tagore, A., Tortora, C., Tuccillo, D., Valentín, M. B., Velasco-Forero, S., Verdoes Kleijn, G. A., & Vernardos, G.
Affiliations
  • Osservatorio astronomico di Bologna (IT)
  • University of Chicago (US)
  • Federal Center for Technological Education Celso Suckow da Fonseca (BR)
  • Centre National de la Recherche Scientifique (FR)
  • École Polytechnique Fédérale de Lausanne (CH)
  • The Open University (GB)
  • University of Manchester (GB)
  • University of Groningen (NL)
  • Carnegie Mellon University (US)
  • Centro Brasileiro de Pesquisas Físicas (BR)
  • University of Nottingham (GB)
  • JPMorgan Chase & Co (United States) (US)
  • Kavli Institute for the Physics and Mathematics of the Universe (JP)
  • Université Paris Sciences et Lettres (FR)
ORCID
Venue
Astronomy & Astrophysics
Publisher
EDP Sciences
Published
2019-05
Volume
625
Pages
A119
ISSN
  • 0004-6361 (ISSN-L)
  • 1432-0746
DOI
10.1051/0004-6361/201832797
OpenAlex
W2786398839
Cited by
152
References
115
Citations by year
2026: 13 · 2025: 23 · 2024: 19 · 2023: 23 · 2022: 20 · 2021: 21 · 2020: 17 · 2019: 12 · 2018: 4
FWCI
9.34
Type
journal article
Language
en
Open access
Licence
www.edpsciences.org/en/authors/copyright-and-licensing (vor, 21 d)
Locations
  • Astronomy and Astrophysics (publishedVersion)
  • arXiv (Cornell University) (acceptedVersion)
  • Open Research Online - ORO (The Open University) (acceptedVersion)
  • Springer Link (Chiba Institute of Technology) (submittedVersion)
  • HAL (submittedVersion, other-oa)
  • Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, other-oa)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
  • Swinburne figshare (Swinburne University of Technology) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Science Foundation (1125897)
  • Kavli Foundation (NSF PHY-1125897)
  • Natural Science Foundation of Shanghai (15ZR1446700)
  • University of Chicago (NSF PHY-1125897, PHY-1125897)
  • Ministry of Education, Culture, Sports, Science and Technology
  • National Natural Science Foundation of China (11503064)
  • Science and Technology Facilities Council (ST/P000584/1)
  • FP7 Ideas: European Research Council (259349)
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, false positives, support vector machine, convolutional neural networks, multi-band imaging

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