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CMU DeepLens: deep learning for automatic image-based galaxy–galaxy strong lens finding
Open accessAbstract
Galaxy-scale strong gravitational lensing can not only provide a valuable probe of the dark matter distribution of massive galaxies, but also provide valuable cosmological constraints, either by studying the population of strong lenses or by measuring time delays in lensed quasars. Due to the rarity of galaxy-scale strongly lensed systems, fast and reliable automated lens finding methods will be essential in the era of large surveys such as Large Synoptic Survey Telescope, Euclid and Wide-Field Infrared Survey Telescope. To tackle this challenge, we introduce CMU DeepLens, a new fully automated galaxy–galaxy lens finding method based on deep learning. This supervised machine learning approach does not require any tuning after the training step which only requires realistic image simulations of strongly lensed systems. We train and validate our model on a set of 20 000 LSST-like mock observations including a range of lensed systems of various sizes and signal-to-noise ratios (S/N). We find on our simulated data set that for a rejection rate of non-lenses of 99 per cent, a completeness of 90 per cent can be achieved for lenses with Einstein radii larger than 1.4 arcsec and S/N larger than 20 on individual g-band LSST exposures. Finally, we emphasize the importance of realistically complex simulations for training such machine learning methods by demonstrating that the performance of models of significantly different complexities cannot be distinguished on simpler simulations. We make our code publicly available at https://github.com/McWilliamsCenter/CMUDeepLens.
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“Deep learning has rapidly become a powerful tool in strong-lensing studies, demonstrating success in tasks ranging from automated lens detection to parameter inference (Lanusse et al., 2018; Hezaveh et al., 2017; Perreault-Levasseur et al., 2017).”
From Masked Autoencoder Pretraining on Strong-Lensing Images for Joint Dark-Matter Model Classification and Super-Resolution (2025) · cites this paper as Lanusse et al., 2018
In the citing paper
“Convolutional neural networks (CNNs) have been used to identify strong lens candidates in wide-field imaging surveys (Lanusse et al., 2018; Jacobs et al., 2019; Schuldt et al., 2021)”
From Masked Autoencoder Pretraining on Strong-Lensing Images for Joint Dark-Matter Model Classification and Super-Resolution (2025) · cites this paper as Lanusse et al., 2018
In the citing paper
“The work by Lanusse et al. (2018) highlights the importance of using realistically complex simulations for training in order to limit the potential biases.”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Lanusse et al. 2018
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
(2014) · Cited by 33,419
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741
Advances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653
(2016) · Cited by 11,679
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_38 · Cited by 10,741
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382
Research works printed but not linked: 7
- Connolly, 2010, Proc. SPIE Conf. Ser. Vol. 4477, Modeling, Systems Engineering, and Project Management for Astronomy IV, 77381O–77381O–10
- Kingma, 2015, International Conference on Learning Representations 2015
- Koekemoer, 2011, ApJS, 197
- Nair, 2010, Proceedings of the 27th International Conference on Machine Learning, Rectified Linear Units Improve Restricted Boltzmann Machines, 807
- Oguri, 2010, MNRAS, 405, 2579
- Petrillo, 2017, MNRAS
- Srivastava, 2014, J. Mach. Learn. Res., 15, 1929
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Metadata
- Authors
- Lanusse, F., Ma, Q., Li, N., Collett, T. E., Li, C.-L., Ravanbakhsh, S., Mandelbaum, R., & Póczos, B.
- Affiliations
- Carnegie Mellon University (US)
- Argonne National Laboratory (US)
- University of Portsmouth (GB)
- School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Corresponding author
- Lanusse, François
- Venue
- Monthly Notices of the Royal Astronomical Society
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-07-07
- Volume
- 473
- Issue
- 3
- Pages
- 3895-3906
- ISSN
- 0035-8711 (ISSN-L)
- 1365-2966
- OpenAlex
- W2604506260
- Cited by
- 191
- References
- 61
- Citations by year
- 2026: 9 · 2025: 20 · 2024: 24 · 2023: 16 · 2022: 29 · 2021: 24 · 2020: 19 · 2019: 31 · 2018: 14 · 2017: 5
- FWCI
- 10.47
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Monthly Notices of the Royal Astronomical Society (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Department of Energy (DESC0011114)
- National Science Foundation under (IIS1563887)
- 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
- deep learning, mock observations, LSST, lensed quasars, time delay, dark matter distribution, supervised learning, image simulation
About these checks
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.
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