Detecting gravitational lenses using machine learning: exploring interpretability and sensitivity to rare lensing configurations
Open accessAbstract
Forthcoming large imaging surveys such as Euclid and the Vera Rubin Observatory Legacy Survey of Space and Time are expected to find more than 105 strong gravitational lens systems, including many rare and exotic populations such as compound lenses, but these 105 systems will be interspersed among much larger catalogues of ∼109 galaxies. This volume of data is too much for visual inspection by volunteers alone to be feasible and gravitational lenses will only appear in a small fraction of these data which could cause a large amount of false positives. Machine learning is the obvious alternative but the algorithms’ internal workings are not obviously interpretable, so their selection functions are opaque and it is not clear whether they would select against important rare populations. We design, build, and train several convolutional neural networks (CNNs) to identify strong gravitational lenses using VIS, Y, J, and H bands of simulated data, with F1 scores between 0.83 and 0.91 on 100 000 test set images. We demonstrate for the first time that such CNNs do not select against compound lenses, obtaining recall scores as high as 76 per cent for compound arcs and 52 per cent for double rings. We verify this performance using Hubble Space Telescope and Hyper Suprime-Cam data of all known compound lens systems. Finally, we explore for the first time the interpretability of these CNNs using Deep Dream, Guided Grad-CAM, and by exploring the kernels of the convolutional layers, to illuminate why CNNs succeed in compound lens selection.
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Keeping the first layers from pretrained Zoobot ensures the model is encouraged to learn true morphological properties rather than other patterns such as PSF orientation.
“By keeping the first layers from the pretrained Zoobot , this also ensures that the model is encouraged to learn true morphological properties rather than other patterns that may arise in simulated lenses such as PSF orientation (Wilde et al. 2022).”
From Euclid Quick Data Release (Q1): XXVIII. The Strong Lensing Discovery Engine C – Finding lenses with machine learning (2026) · cites this paper as Wilde et al. 2022
Section 6 (Discussion; page not provided) reports that the CNN learned to classify images using the orientation of the simulated Euclid PSF rather than a scientifically relevant feature. Sections 3.1–3.2 describe the paper’s own CNN architectures and training, but do not say that retaining pretrained Zoobot layers encourages learning true morphology or prevents reliance on PSF orientation. (LLM, conf=0.86)
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- Authors
- Wilde, J., Serjeant, S., Bromley, J. M., Dickinson, H., Koopmans, L. V. E., & Metcalf, R. B.
- Affiliations
- The Open University (GB)
- University of Groningen (NL)
- Osservatorio di Astrofisica e Scienza dello Spazio (IT)
- Corresponding author
- Wilde, Joshua
- ORCID
- Venue
- Monthly Notices of the Royal Astronomical Society
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-02-08
- Volume
- 512
- Issue
- 3
- Pages
- 3464-3479
- ISSN
- 0035-8711 (ISSN-L)
- 1365-2966
- OpenAlex
- W4214939473
- Cited by
- 31
- References
- 79
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 7 · 2023: 7 · 2022: 3
- FWCI
- 2.45
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Monthly Notices of the Royal Astronomical Society (publishedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, other-oa)
- Open Research Online (The Open University) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, other-oa)
- Open Research Online (The Open University) (submittedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- Horizon 2020 (824064, ST/P000584/1)
- 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 › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- strong gravitational lensing, compound lenses, double rings, convolutional neural networks, interpretability, Guided Grad-CAM, Euclid, Vera C. Rubin Observatory
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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.
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