Finding strong lenses in CFHTLS using convolutional neural networks
Open accessAbstract
We train and apply convolutional neural networks, a machine learning technique developed to learn from and classify image data, to Canada–France–Hawaii Telescope Legacy Survey (CFHTLS) imaging for the identification of potential strong lensing systems. An ensemble of four convolutional neural networks was trained on images of simulated galaxy–galaxy lenses. The training sets consisted of a total of 62 406 simulated lenses and 64 673 non-lens negative examples generated with two different methodologies. An ensemble of trained networks was applied to all of the 171 deg2 of the CFHTLS wide field image data, identifying 18 861 candidates including 63 known and 139 other potential lens candidates. A second search of 1.4 million early-type galaxies selected from the survey catalogue as potential deflectors, identified 2465 candidates including 117 previously known lens candidates, 29 confirmed lenses/high-quality lens candidates, 266 novel probable or potential lenses and 2097 candidates we classify as false positives. For the catalogue-based search we estimate a completeness of 21–28 per cent with respect to detectable lenses and a purity of 15 per cent, with a false-positive rate of 1 in 671 images tested. We predict a human astronomer reviewing candidates produced by the system would identify 20 probable lenses and 100 possible lenses per hour in a sample selected by the robot. Convolutional neural networks are therefore a promising tool for use in the search for lenses in current and forthcoming surveys such as the Dark Energy Survey and the Large Synoptic Survey Telescope.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
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 · see citing works
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928 · see citing works
Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891 · see citing works
Machine learning: Trends, perspectives, and prospects
Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955 · see citing works
(2010) · Cited by 8,261 · see citing works
Proceedings of the 25th international conference on Machine learning - ICML '08
(2008) · doi:10.1145/1390156 · Cited by 6,865 · see citing works
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 · see citing works
Large-Scale Machine Learning with Stochastic Gradient Descent
Bottou, L. (2010) · Proceedings of COMPSTAT'2010 · doi:10.1007/978-3-7908-2604-3_16 · Cited by 5,795 · see citing works
Fukushima, K. (1980) · Biological Cybernetics · doi:10.1007/bf00344251 · Cited by 5,759 · see citing works
ADADELTA: An Adaptive Learning Rate Method
Zeiler, M. D. (2012) · arXiv · doi:10.48550/arxiv.1212.5701 · Cited by 5,322 · see citing works
Caffe: Convolutional Architecture for Fast Feature EmbeddingPreprint
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.5093 · Cited by 4,749 · see citing works
LSST: from Science Drivers to Reference Design and Anticipated Data Products
Ivezić, Ž., Kahn, S. M., Tyson, J. A., Abel, B., Acosta, E., Allsman, R., Alonso, D., AlSayyad, Y., Anderson, S. F., Andrew, J., et al. (2008) · arXiv · doi:10.48550/arxiv.0805.2366 · Cited by 4,357 · see citing works
LSST: From Science Drivers to Reference Design and Anticipated Data Products
Ivezić, Ž., Kahn, S. M., Tyson, J. A., Abel, B., Acosta, E., Allsman, R., Alonso, D., AlSayyad, Y., Anderson, S. F., Andrew, J., et al. (2019) · The Astrophysical Journal · doi:10.3847/1538-4357/ab042c · Cited by 4,357 · see citing works
Hansen, L. K., & Salamon, P. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.58871 · Cited by 4,260 · see citing works
Human-level concept learning through probabilistic program induction
Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015) · Science · doi:10.1126/science.aab3050 · Cited by 2,817 · see citing works
Theory of the backpropagation neural network
Hecht-Nielsen (1989) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.1989.118638 · Cited by 2,221 · see citing works
Ensembling neural networks: Many could be better than all
Zhou, Z.-H., Wu, J., & Tang, W. (2002) · Artificial Intelligence · doi:10.1016/s0004-3702(02)00190-x · Cited by 1,964 · see citing works
Learning to rank: from pairwise approach to listwise approach
Cao, Z., Qin, T., Liu, T.-Y., Tsai, M.-F., & Li, H. (2007) · Proceedings of the 24th international conference on Machine learning · doi:10.1145/1273496.1273513 · Cited by 1,960 · see citing works
What's In A Name? Malay Seals As Onomastic Sources
Gallop, A. T. (2018) · Malay Literature · doi:10.37052/ml.31(1)no1 · Cited by 1,495 · see citing works
Refsdal, S. (1964) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/128.4.307 · Cited by 1,067 · see citing works
0957 + 561 A, B: twin quasistellar objects or gravitational lens?
Walsh, D., Carswell, R. F., & Weymann, R. J. (1979) · Nature · doi:10.1038/279381a0 · Cited by 994 · see citing works
A combined SVM and LDA approach for classification
Xiong, T., & Cherkassky, V. (2006) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2005.1556089 · Cited by 984 · see citing works
Lens-Like Action of a Star by the Deviation of Light in the Gravitational Field
Einstein, A. (1936) · Science · doi:10.1126/science.84.2188.506 · Cited by 961 · see citing works
Rotation-invariant convolutional neural networks for galaxy morphology prediction
Dieleman, S., Willett, K. W., & Dambre, J. (2015) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stv632 · Cited by 781 · see citing works
Gravitational Lensing: Strong, Weak and Micro
Schneider, P., Kochanek, C. S., & Wambsganß, J. (2006) · Saas-Fee Advanced Course · doi:10.1007/978-3-540-30310-7 · Cited by 632 · see citing works
Auger, M. W., Treu, T., Bolton, A. S., Gavazzi, R., Koopmans, L. V. E., Marshall, P. J., Moustakas, L. A., & Burles, S. M. (2010) · The Astrophysical Journal · doi:10.1088/0004-637x/724/1/511 · Cited by 570 · see citing works
Treu, T. (2010) · Annual Review of Astronomy and Astrophysics · doi:10.1146/annurev-astro-081309-130924 · Cited by 467 · see citing works
Bonvin, V., Courbin, F., Suyu, S. H., Marshall, P. J., Rusu, C. E., Sluse, D., Tewes, M., Wong, K. C., Collett, T., Fassnacht, C. D., et al. (2017) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stw3006 · Cited by 457 · see citing works
Self-citationMassive Dark Matter Halos and Evolution of Early‐Type Galaxies toz≈ 1
Treu, T., & Koopmans, L. V. E. (2004) · The Astrophysical Journal · doi:10.1086/422245 · Cited by 442 · see citing works
Gravitationally lensed quasars and supernovae in future wide-field optical imaging surveys
Oguri, M., & Marshall, P. J. (2010) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2010.16639.x · Cited by 433 · see citing works
Cosmological Applications of Gravitational Lensing
Blandford, R. D., & Narayan, R. (1992) · Annual Review of Astronomy and Astrophysics · doi:10.1146/annurev.aa.30.090192.001523 · Cited by 426 · see citing works
Flaugher, B. (2005) · International Journal of Modern Physics A · doi:10.1142/s0217751x05025917 · Cited by 391 · see citing works
TWO ACCURATE TIME-DELAY DISTANCES FROM STRONG LENSING: IMPLICATIONS FOR COSMOLOGY
Suyu, S. H., Auger, M. W., Hilbert, S., Marshall, P. J., Tewes, M., Treu, T., Fassnacht, C. D., Koopmans, L. V. E., Sluse, D., Blandford, R. D., et al. (2013) · The Astrophysical Journal · doi:10.1088/0004-637x/766/2/70 · Cited by 366 · see citing works
THE POPULATION OF GALAXY–GALAXY STRONG LENSES IN FORTHCOMING OPTICAL IMAGING SURVEYS
Collett, T. E. (2015) · The Astrophysical Journal · doi:10.1088/0004-637x/811/1/20 · Cited by 354 · see citing works
Self-citationA magnified young galaxy from about 500 million years after the Big Bang
Zheng, W., Postman, M., Zitrin, A., Moustakas, J., Shu, X., Jouvel, S., Høst, O., Molino, A., Bradley, L., Coe, D., et al. (2012) · Nature · doi:10.1038/nature11446 · Cited by 314 · see citing works
A CATALOG OF VISUAL-LIKE MORPHOLOGIES IN THE 5 CANDELS FIELDS USING DEEP LEARNING
Huertas-Company, M., Gravet, R., Cabrera-Vives, G., Pérez-González, P. G., Kartaltepe, J. S., Barro, G., Bernardi, M., Mei, S., Shankar, F., Dimauro, P., et al. (2015) · The Astrophysical Journal Supplement Series · doi:10.1088/0067-0049/221/1/8 · Cited by 285 · see citing works
Gravitational Lensing in Astronomy
Wambsganß, J. (1998) · Living Reviews in Relativity · doi:10.12942/lrr-1998-12 · Cited by 284 · see citing works
Treu, T., & Koopmans, L. V. E. (2002) · The Astrophysical Journal · doi:10.1086/341216 · Cited by 265 · see citing works
Schawinski, K., Zhang, C., Zhang, H., Fowler, L., & Santhanam, G. K. (2017) · Monthly Notices of the Royal Astronomical Society: Letters · doi:10.1093/mnrasl/slx008 · Cited by 246 · see citing works
Internal and Collective Properties of Galaxies in the Sloan Digital Sky Survey
Choi, Y., Park, C., & Vogeley, M. S. (2007) · The Astrophysical Journal · doi:10.1086/511060 · Cited by 243 · see citing works
Nebulae as Gravitational Lenses
Zwicky, F. (1937) · Physical Review · doi:10.1103/physrev.51.290 · Cited by 242 · see citing works
Sonnenfeld, A., Treu, T., Gavazzi, R., Suyu, S. H., Marshall, P. J., Auger, M. W., & Nipoti, C. (2013) · The Astrophysical Journal · doi:10.1088/0004-637x/777/2/98 · Cited by 216 · see citing works
CMU DeepLens: deep learning for automatic image-based galaxy–galaxy strong lens finding
Lanusse, F., Ma, Q., Li, N., Collett, T. E., Li, C.-L., Ravanbakhsh, S., Mandelbaum, R., & Póczos, B. (2018) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stx1665 · Cited by 191 · see citing works
Self-citationFinding strong gravitational lenses in the Kilo Degree Survey with Convolutional Neural Networks
Petrillo, C. E., Tortora, C., Chatterjee, S., Vernardos, G., Koopmans, L. V. E., Kleijn, G. V., Napolitano, N. R., Covone, G., Schneider, P., Grado, A., et al. (2017) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stx2052 · Cited by 189 · see citing works
The CFHTLS strong lensing legacy survey
Cabanac, R., Alard, C., Dantel-Fort, M., Fort, B. P., Gavazzi, R., Gómez, P., Kneib, J., Fèvre, O. C. L., Mellier, Y., Pelló, R., et al. (2006) · Astronomy and Astrophysics · doi:10.1051/0004-6361:20065810 · Cited by 185 · see citing works
More, A., Cabanac, R., More, S., Alard, C., Limousin, M., Kneib, J., Gavazzi, R., & Motta, V. (2012) · The Astrophysical Journal · doi:10.1088/0004-637x/749/1/38 · Cited by 167 · see citing works
Self-citationSonnenfeld, A., Treu, T., Marshall, P. J., Suyu, S. H., Gavazzi, R., Auger, M. W., & Nipoti, C. (2015) · The Astrophysical Journal · doi:10.1088/0004-637x/800/2/94 · Cited by 162 · see citing works
The luminosity and stellar mass Fundamental Plane of early-type galaxies
Hyde, J. B., & Bernardi, M. (2009) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2009.14783.x · Cited by 158 · see citing works
COSMOGRAIL: the COSmological MOnitoring of GRAvItational Lenses
Tewes, M., Courbin, F., Meylan, G. L., Kochanek, C. S., Eulaers, E., Cantale, N., Mosquera, A. M., Magain, P., Winckel, H. V., Sluse, D., et al. (2013) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201220352 · Cited by 150 · see citing works
Measuring photometric redshifts using galaxy images and Deep Neural Networks
Hoyle, B. (2016) · Astronomy and Computing · doi:10.1016/j.ascom.2016.03.006 · Cited by 144 · see citing works
Quider, A. M., Pettini, M., Shapley, A. E., & Steidel, C. C. (2009) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2009.15234.x · Cited by 141 · see citing works
Cosmological constraints from the double source plane lens SDSSJ0946+1006
Collett, T. E., & Auger, M. W. (2014) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stu1190 · Cited by 140 · see citing works
Self-citationGavazzi, R., Marshall, P. J., Treu, T., & Sonnenfeld, A. (2014) · The Astrophysical Journal · doi:10.1088/0004-637x/785/2/144 · Cited by 125 · see citing works
B1422+231: The influence of mass substructure on strong lensing
Bradač, M., Schneider, P., Steinmetz, M., Lombardi, M. J., King, L. J., & Porcas, R. W. (2002) · Astronomy and Astrophysics · doi:10.1051/0004-6361:20020559 · Cited by 123 · see citing works
Oguri, M., Rusu, C. E., & Falco, E. E. (2014) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stu106 · Cited by 121 · see citing works
Space Warps – I. Crowdsourcing the discovery of gravitational lenses
Marshall, P. J., Verma, A., More, A., Davis, C. P., More, S., Kapadia, A., Parrish, M., Snyder, C., Wilcox, J., Baeten, E., et al. (2016) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stv2009 · Cited by 117 · see citing works
Self-citationMore, A., Verma, A., Marshall, P. J., More, S., Baeten, E., Wilcox, J., Macmillan, C., Cornen, C., Kapadia, A., Parrish, M., et al. (2016) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stv1965 · Cited by 116 · see citing works
Self-citationThe CFHTLS Strong Lensing Legacy Survey: I. Survey overview and T0002 release samplePreprint
Cabanac, R. A., Alard, C., Dantel-Fort, M., et al. (2006) · arXiv (Cornell University) · doi:10.48550/arxiv.astro-ph/0610362 · Cited by 104 · see citing works
Oguri, M., Inada, N., Strauss, M. A., Kochanek, C. S., Kayo, I., Shin, M., Morokuma, T., Richards, G. T., Rusu, C. E., Frieman, J. A., et al. (2012) · The Astronomical Journal · doi:10.1088/0004-6256/143/5/120 · Cited by 89 · see citing works
AUTOMATED DETECTION OF GALAXY-SCALE GRAVITATIONAL LENSES IN HIGH-RESOLUTION IMAGING DATA
Marshall, P. J., Hogg, D. W., Moustakas, L. A., Fassnacht, C. D., Bradač, M., Schrabback, T., & Blandford, R. D. (2009) · The Astrophysical Journal · doi:10.1088/0004-637x/694/2/924 · Cited by 85 · see citing works
Arcfinder: an algorithm for the automatic detection of gravitational arcs
Seidel, G., & Bartelmann, M. (2007) · Astronomy and Astrophysics · doi:10.1051/0004-6361:20066097 · Cited by 80 · see citing works
The luminosity and stellar mass Fundamental Plane of early-type galaxiesPreprint
Hyde, J. B., & Bernardi, M. (2009) · Cited by 77 · see citing works
Ostrovski, F., McMahon, R. G., Connolly, A. J., Lemon, C., Auger, M. W., Banerji, M., Hung, J., Koposov, S. E., Lidman, C. E., Reed, S. L., et al. (2016) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stw2958 · Cited by 73 · see citing works
Computational Methods for Gravitational LensingPreprint
Keeton, C. R. (2001) · arXiv (Cornell University) · doi:10.48550/arxiv.astro-ph/0102340 · Cited by 71 · see citing works
Lechevallier, Y., & Saporta, G. (2010) · Physica-Verlag eBooks · doi:10.1007/978-3-7908-2604-3 · Cited by 69 · see citing works
Euclidmission: building of a reference survey
Amiaux, J., Scaramella, R., Mellier, Y., Altieri, B., Burigana, C., Silva, A. D., Gómez, P. V., Hoar, J., Laureijs, R., Maiorano, E., et al. (2012) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.926513 · Cited by 59 · see citing works
Newton, E. R., Marshall, P. J., Treu, T., Auger, M. W., Gavazzi, R., Bolton, A. S., Koopmans, L. V. E., & Moustakas, L. A. (2011) · The Astrophysical Journal · doi:10.1088/0004-637x/734/2/104 · Cited by 58 · see citing works
Estrada, J., Annis, J. T., Diehl, H. T., Hall, P. B., Las, T., Lin, H., Makler, M., Merritt, K. W., Scarpine, V., Allam, S., et al. (2007) · The Astrophysical Journal · doi:10.1086/512599 · Cited by 53 · see citing works
Automatic detection of arcs and arclets formed by gravitational lensing
Lenzen, F., Schindler, S., & Scherzer, O. (2004) · Astronomy and Astrophysics · doi:10.1051/0004-6361:20034619 · Cited by 52 · see citing works
A PCA-based automated finder for galaxy-scale strong lenses
Joseph, R., Courbin, F., Metcalf, R. B., Giocoli, C., Hartley, P., Jackson, N. J., Bellagamba, F., Kneib, J., Koopmans, L. V. E., Lemson, G., et al. (2014) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201423365 · Cited by 48 · see citing works
Chitah: STRONG-GRAVITATIONAL-LENS HUNTER IN IMAGING SURVEYS
Chan, J. H. H., Suyu, S. H., Chiueh, T., More, A., Marshall, P. J., Coupon, J., Oguri, M., & Price, P. (2015) · The Astrophysical Journal · doi:10.1088/0004-637x/807/2/138 · Cited by 47 · see citing works
Self-citationConnolly, A. J., Peterson, J. R., Jernigan, J. G., Abel, R., Bankert, J., Chang, C., Claver, C. F., Gibson, R. R., Gilmore, D. K., Grace, E., et al. (2010) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.857819 · Cited by 40 · see citing works
A neural network gravitational arc finder based on the Mediatrix filamentation method
Bom, C. R., Makler, M., de Albuquerque, M. P., & Brandt, C. (2016) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201629159 · Cited by 38 · see citing works
Gravitational Lensing: Einstein’s unfinished symphony
Treu, T., & Ellis, R. S. (2015) · Contemporary Physics · doi:10.1080/00107514.2015.1006001 · Cited by 31 · see citing works
A search for edge-on galaxy lenses in the CFHT Legacy Survey
Sygnet, J.-F., Tu, H., Fort, B. P., & Gavazzi, R. (2010) · Astronomy and Astrophysics · doi:10.1051/0004-6361/200913977 · Cited by 29 · see citing works
A PCA-based automated finder for galaxy-scale strong lensesPreprint
Joseph, R., Courbin, F., Metcalf, R. B., Giocoli, C., Hartley, P., Jackson, N. J., Bellagamba, F., Kneib, J., Koopmans, L. V. E., Lemson, G., et al. (2014) · Cited by 29 · see citing works
Extensive light profile fitting of galaxy-scale strong lenses
Brault, F. L. G., & Gavazzi, R. (2015) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201425275 · Cited by 20 · see citing works
Automated detection of gravitational arcsPreprint
Alard, C. (2006) · arXiv (Cornell University) · doi:10.48550/arxiv.astro-ph/0606757 · Cited by 16 · see citing works
Search for gravitational lens candidates in the XMM-LSS/CFHTLS common field
Elyiv, A., Melnyk, O., Finet, F., Pospieszalska-Surdej, A., Chiappetti, L., Pierre, M., Sadibekova, T., & Surdej, J. (2013) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stt1255 · Cited by 7 · see citing works
Flagged first, then by citations
2 of the 83 references couldn’t be matched to a known paper.
Research works printed but not linked: 13
- Collett T. E., et al., 2017, arXiv:1703.08410 [astro-ph] · arXiv
- Glorot X., Bengio Y., 2010, in International Conference on Artificial Intel- ligence and Statistics. pp 249-256
- Goodfellow I., Pouget-Abadie J., Mirza M., Xu B., Warde-Farley D., Ozair S., Courville A., Bengio Y., 2014, in Ghahramani Z., Welling M., Cortes C., Lawrence N. D., Weinberger K. Q., eds, , Advances in Neural Information Processing Systems 27.
- Hansen L. K., Salamon P., 1990, IEEE Trans. Pattern Anal. Mach. Intell., 12, 993
- Krizhevsky A., Sutskever I., Hinton G. E., 2012, in Pereira F., Burges C. J. C., Bottou L., Weinberger K. Q., eds, , Advances in Neural Informa- tion Processing Systems 25.
- Lanusse F., Ma Q., Li N., Collett T. E., Li C.-L., Ravanbakhsh S., Mandel- baum R., Poczos B., 2017, preprint, 1703, arXiv:1703.02642 · arXiv
- Lecun Y., Bottou L., Bengio Y., Haffner P., 1998, Proc. IEEE, 86, 2278
- Nair V., Hinton G. E., 2010, in Proceedings of the 27th International Con- ference on Machine Learning (ICML-10). pp 807-814
- Petrillo C. E., et al., 2017, arXiv:1702.07675 [astro-ph] · arXiv
- Schawinski K., Zhang C., Zhang H., Fowler L., Santhanam G. K., 2017, arXiv:1702.00403 [astro-ph, stat] · arXiv
- Szegedy C., Reed S., Erhan D., Anguelov D., 2014, ArXiv Prepr. ArXiv14121441
- Thanjavur K. G., 2009, PhD Thesis. University of Victoria, Canada The DES Collaboration 2005, arXiv:astro-ph/0510346 · arXiv
- Zeiler M. D., Fergus R., 2014, in Fleet D., Pajdla T., Schiele B., Tuytelaars T., eds, , Vol. 8689, Computer Vision -ECCV 2014. Springer Interna- tional Publishing, Cham, pp 818-833
Cited by
Sorted by citations
Birrer, S., Treu, T., Rusu, C. E., Bonvin, V., Fassnacht, C. D., Chan, J. H. H., Agnello, A., Shajib, A. J., Chen, G. C.-F., Auger, M. W., et al. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz200 · Cited by 365 · see citing works
lenstronomy: Multi-purpose gravitational lens modelling software package
Birrer, S., & Amara, A. (2018) · Physics of the Dark Universe · doi:10.1016/j.dark.2018.11.002 · Cited by 324 · see citing works
“ppendix A). From the current and upcoming surveys, the sample of known strong lenses is rapidly increasing (see e.g. Agnello et al., 2015; Nord et al., 2016; Schechter et al., 2017; Lin et al., 2017; Jacobs et al., 2017; Ostrovski et al., 2017; Williams et al., 2018; Lemon et al., 2018) and Treu et al. (submitted). Th…”
BackgroundFast automated analysis of strong gravitational lenses with convolutional neural networks
Hezaveh, Y. D., Levasseur, L. P., & Marshall, P. J. (2017) · Nature · doi:10.1038/nature23463 · Cited by 261 · see citing works
The strong gravitational lens finding challenge
Metcalf, R. B., Meneghetti, M., Avestruz, C., Bellagamba, F., Bom, C. R., Bertin, E., Cabanac, R., Courbin, F., Davies, A., Decencière, E., et al. (2019) · Astronomy & Astrophysics · doi:10.1051/0004-6361/201832797 · Cited by 152 · see citing works
“…(Dieleman et al. 2015), photometric redshifts (Cavuoti et al. 2017; Sadeh et al. 2016; Samui & Samui 2017), supernova classification (Lochner et al. 2016) and the lens finding problem (Petrillo et al. 2017; Jacobs et al. 2017; Ostrovski et al. 2017; Bom et al. 2017; Hartley et al. 2017).”
BackgroundSelf-citationSurveying the reach and maturity of machine learning and artificial intelligence in astronomy
Fluke, C. J., & Jacobs, C. (2020) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1349 · Cited by 149 · see citing works
“…Ciuca and Hern´andez, 2017, Gomez Gonzalez et al., 2018, Pourrahmani et al., 2018, Lanusse et al., 2018, Wan et al., 2018, Morello et al., 2018, Jacobs et al., 2017]; spectroscopy [Bu et al., 2017, Li et al., 2018]; photometry [Timlin et al., 2018, Vida and Roettenbacher, 2018, Ostrovski et…”
BackgroundInfluentialSelf-citationLima, M. V. B. T. (2019) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · doi:10.3847/1538-4365/ab26b6/pdf · Cited by 135 · see citing works
Perreault Levasseur, L., Hezaveh, Y. D., & Wechsler, R. H. (2017) · The Astrophysical Journal Letters · doi:10.3847/2041-8213/aa9704 · Cited by 145 · see citing works
“Recently, neural networks were applied to the discovery of lenses (Jacobs et al. 2017; Lanusse et al. 2017; Petrillo et al. 2017) and to the simulation of weakly lensed galaxy images (Ravanbakhsh et al. 2016).”
MethodMachine-learning-based Brokers for Real-time Classification of the LSST Alert Stream
Narayan, G., Zaidi, T., Soraisam, M., Wang, Z., Lochner, M., Matheson, T., Saha, A., Yang, S., Zhao, Z., Kececioglu, J., et al. (2018) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/aab781 · Cited by 106 · see citing works
“…m -∆ t “images” of light curves belonging to different classes of variable stars were then used to train a convolutional neural network, often used for image recognition and classification in astronomy (e.g., Dieleman et al. 2015; Jacobs et al. 2017), and achieved acceptable results for some classes.”
MethodPetrillo, C. E., Tortora, C., Vernardos, G., Koopmans, L. V. E., Kleijn, G. V., Bilicki, M., Napolitano, N. R., Chatterjee, S., Covone, G., Dvornik, A., et al. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz189 · Cited by 108 · see citing works
The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys
Huertas-Company, M., & Lanusse, F. (2023) · Publications of the Astronomical Society of Australia · doi:10.1017/pasa.2022.55 · Cited by 102 · see citing works
“An example of application which has significantly benefited from the advent of deep learning is the detection of strong gravitational lenses (e.g. Jacobs et al., 2017; Petrillo et al., 2017; Lanusse et al., 2018; Davies et al., 2019; Schaefer et al., 2018; Metcalf et al., 2019; Petrillo et al.,…”
MethodFinding high-redshift strong lenses in DES using convolutional neural networks
Jacobs, C., Collett, T. E., Glazebrook, K., McCarthy, C. D., Qin, A. K., Abbott, T. M. C., Abdalla, F. B., Annis, J. T., Avila, S., Bechtol, K., et al. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz272 · Cited by 103 · see citing works
“This network architecture was chosen based on previous experience (Jacobs et al. 2017), and was deemed fit-for-purpose based on the high accuracy realised during the training process.”
BackgroundMethodInfluentialSelf-citationFinding Strong Gravitational Lenses in the DESI DECam Legacy Survey
Huang, X., Storfer, C., Ravi, V., Pilon, A., Domingo, M., Schlegel, D. J., Bailey, S., Dey, A. T., Gupta, R. R., Herrera, D., et al. (2020) · The Astrophysical Journal · doi:10.3847/1538-4357/ab7ffb · Cited by 104 · see citing works
“In previous applications of CNNs to real observations, training samples are constructed from simulated lens images, combined with observed (Petrillo et al. 2017), simulated (Jacobs et al. 2017; Pourrahmani et al. 2018), or a mixture of observed and simulated non-lenses (Jacobs et al. 2019).”
BackgroundMethodGalaxy detection and identification using deep learning and data augmentation
Gonz'alez, R., Muñoz, R., & Hern'andez, C. A. (2018) · Astronomy and Computing · doi:10.1016/j.ascom.2018.09.004 · Cited by 76 · see citing works
“, 2017) for LIGO detections and using convolutional neural network (CNN) in identifying strong lenses in imaging data (Jacobs et al., 2017).”
BackgroundDiscovering New Strong Gravitational Lenses in the DESI Legacy Imaging Surveys
Huang, X., Storfer, C., Gu, A., Ravi, V., Pilon, A., Sheu, W., Venguswamy, R., Banka, S., Dey, A., Landriau, M., et al. (2020) · Astrophysical Journal · doi:10.3847/1538-4357/abd62b · Cited by 60 · see citing works
Wang, G.-J., Ma, X.-J., Li, S.-Y., & Xia, J.-Q. (2019) · Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ab620b · Cited by 90 · see citing works
“…al. 2018, 2019; Ntampaka et al. 2019; Ribli et al. 2019), searching and estimating parameters of strong gravitational lenses (Hezaveh et al. 2017; Jacobs et al. 2017; Petrillo et al. 2017; Pourrahmani et al. 2018; Schaefer et al. 2018), classifying the large-scale structure of the universe…”
BackgroundDeep convolutional neural networks as strong gravitational lens detectors
Schaefer, C., Geiger, M., Kuntzer, T., & Kneib, J. (2017) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201731201 · Cited by 88 · see citing works
“Recently, Petrillo et al. (2017) and Jacobs et al. (2017) started using convolutional neural networks (CNNs) for lens detection.”
MethodRadio Galaxy Zoo: compact and extended radio source classification with deep learning
Lukic, V, Brüggen, M., Banfield, J. K., Wong, O. I., Rudnick, L., Norris, R. P., & Simmons, B. (2018) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/sty163 · Cited by 86 · see citing works
“…Abdalla & Lahav 2016), differentiating between real vs fake transients in difference imaging using artificial neural networks, random forests and boosted decision trees (Wright et al. 2015) and using convolutional neural networks in identifying strong lenses in imaging data (Jacobs et al. 2017).”
BackgroundCañameras, R., Schuldt, S., Suyu, S. H., Taubenberger, S., Meinhardt, T., Leal-Taixé, L., Lemon, C., Rojas, K., & Savary, E. (2020) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202038219 · Cited by 85 · see citing works
Identifying strong lenses with unsupervised machine learning using convolutional autoencoder
Cheng, T.-Y., Li, N., Conselice, C. J., Aragón‐Salamanca, A., Dye, S., & Metcalf, R. B. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa1015 · Cited by 80 · see citing works
LensFlow: A Convolutional Neural Network in Search of Strong Gravitational Lenses
Pourrahmani, M., Nayyeri, H., & Cooray, A. R. (2018) · The Astrophysical Journal · doi:10.3847/1538-4357/aaae6a · Cited by 79 · see citing works
“The deepness of our ConvNet is comparable to Petrillo et al. (2017) and Jacobs et al. (2017) but it is shallower than Lanusse et al. (2017).”
BackgroundMethodInfluentialNew High-quality Strong Lens Candidates with Deep Learning in the Kilo-Degree Survey
Li, R., Napolitano, N. R., Tortora, C., Spiniello, C., Koopmans, L. V. E., Huang, Z., Roy, N., Vernardos, G., Chatterjee, S., Giblin, B., et al. (2020) · The Astrophysical Journal · doi:10.3847/1538-4357/ab9dfa · Cited by 79 · see citing works
“In fact, more than 1000 lens candidates have been found in the last three years in recent ground-based surveys (e.g., Petrillo et al. 2017, 2019a; Jacobs et al. 2017, 2019; Pourrahmani et al. 2018; Khramtsov et al. 2019), such as the Kilo-Degree Survey (de Jong et al. 2013), the Hyper Suprime-Cam…”
BackgroundData-driven Reconstruction of Gravitationally Lensed Galaxies Using Recurrent Inference Machines
Morningstar, W., Levasseur, L. P., Hezaveh, Y., Blandford, R., Marshall, P., Putzky, P., Rueter, T. D., Wechsler, R., & Welling, M. (2019) · Astrophysical Journal · doi:10.3847/1538-4357/ab35d7 · Cited by 62 · see citing works
“…within the field of gravitational lensing, where CNNs have been used for performing both the tasks of lens finding (e.g. Lanusse et al. 2017; Jacobs et al. 2017; Petrillo et al. 2018; Pourrahmani et al. 2018; Schaefer et al. 2018), and lens modeling (Hezaveh et al. 2017; Perreault Levasseur…”
MethodCheng, T.-Y., Conselice, C., Aragón-Salamanca, A., Aguena, M., Allam, S., Andrade-Oliveira, F., Annis, J., Bluck, A., Brooks, D., Burke, D., et al. (2021) · Monthly notices of the Royal Astronomical Society · doi:10.1093/mnras/stab2142 · Cited by 35 · see citing works
Search of strong lens systems in the Dark Energy Survey using convolutional neural networks
Rojas, K., Savary, É., Clément, B., Maus, M., Courbin, F., Lemon, C., Chan, J. H. H., Vernardos, G., Joseph, R., Cañameras, R., et al. (2022) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202142119 · Cited by 60 · see citing works
Gaia GraL: Gaia DR2 Gravitational Lens Systems
Delchambre, L., Krone-Martins, A., Wertz, O., Ducourant, C., Galluccio, L., Klüter, J., Mignard, F., Teixeira, R., Djorgovski, S. G., Stern, D. K., et al. (2019) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201833802 · Cited by 57 · see citing works
Detecting outliers in astronomical images with deep generative networks
Margalef-Bentabol, B., Huertas-Company, M., Charnock, T., Margalef-Bentabol, C., Bernardi, M., Dubois, Y., Storey-Fisher, K., & Zanisi, L. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa1647 · Cited by 56 · see citing works
“…of labeled or annotated data are available for classification (e.g Huertas-Company et al. 2015; Kim & Brunner 2017; Cabrera-Vives et al. 2017; Jacobs et al. 2017; Sreejith et al. 2018; Domı́nguez Sánchez et al. 2018; Huertas-Company et al. 2018; Davidzon et al. 2019), regression (e.g.…”
BackgroundCañameras, R., Schuldt, S., Shu, Y., Suyu, S. H., Taubenberger, S., Meinhardt, T., Leal-Taixé, L., Chao, D. C.-Y., Inoue, K. T., Jaelani, A. T., et al. (2021) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202141758 · Cited by 55 · see citing works
Self-citationStrong Gravitational Lensing and Microlensing of Supernovae
Suyu, S. H., Goobar, A., Collett, T. E., More, A., & Vernardos, G. (2024) · Space Science Reviews · doi:10.1007/s11214-024-01044-7 · Cited by 53 · see citing works
“…learning techniques such as convolutional neural networks (CNN) perform well and are efficient at processing large amounts of imaging data for finding gravitational lenses (e.g., Jacobs et al. 2017, 2019; Lanusse et al. 2018; Metcalf et al. 2019; Petrillo et al. 2019; Cañameras et al. 2020, 2021).”
BackgroundMethodSelf-citationMining for Strong Gravitational Lenses with Self-supervised Learning
Stein, G., Blaum, J., Harrington, P., Medan, T., & Lukić, Z. (2022) · The Astrophysical Journal · doi:10.3847/1538-4357/ac6d63 · Cited by 52 · see citing works
“In recent years, the search for strong lenses has been dominated by the use of CNNs trained in a standard supervised learning setup (e.g., Jacobs et al. 2017, 2019a, 2019b; Petrillo et al. 2017, 2019; Lanusse et al. 2018; Cañameras et al. 2020, 2021; He et al. 2020; Huang et al. 2020, 2021; Li et…”
BackgroundMethodInfluentialShu, Y., Cañameras, R., Schuldt, S., Suyu, S. H., Taubenberger, S., Inoue, K. T., & Jaelani, A. T. (2022) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202243203 · Cited by 48 · see citing works
Testing Convolutional Neural Networks for finding strong gravitational lenses in KiDS
Petrillo, C. E., Tortora, C., Chatterjee, S., Vernardos, G., Koopmans, L. V. E., Kleijn, G. V., Napolitano, N. R., Covone, G., Kelvin, L. S., & Hopkins, A. (2018) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/sty2683 · Cited by 48 · see citing works
“…et al. 2016; More et al. 2016) where non-professional volunteers can classify galaxy images with the help of a web applet.3 Most recently, Petrillo et al. (2017) and Jacobs et al. (2017) have used Convolutional Neural Network (ConvNets) for finding lens candidates in KiDS and CFHTLS, respectively.”
MethodPearson, J. F., Li, N., & Dye, S. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz1750 · Cited by 45 · see citing works
“Convolutional Neural Networks (CNNs) have also been extensively used in gravitational lens detection (Jacobs et al. 2017; Lanusse et al. 2017; Petrillo et al. 2017; Metcalf et al. 2018; Schaefer et al. 2018) as these do not require spectroscopic data nor arbitrary geometric measurements.”
BackgroundSchuldt, S., Suyu, S. H., Meinhardt, T., Leal-Taixé, L., Cañameras, R., Taubenberger, S., & Halkola, A. (2020) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202039574 · Cited by 42 · see citing works
ECoPANN: A Framework for Estimating Cosmological Parameters Using Artificial Neural Networks
Wang, G.-J., Li, S.-Y., & Xia, J.-Q. (2020) · Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/aba190 · Cited by 34 · see citing works
“…models (Schmelzle et al. 2017; Hassan et al. 2018), searching and estimating parameters of strong gravitational lenses (Hezaveh et al. 2017; Jacobs et al. 2017; Petrillo et al. 2017; Pourrahmani et al. 2018; Schaefer et al. 2018), classifying the large-scale structure of the universe…”
BackgroundDeep Horizon: A machine learning network that recovers accreting black hole parameters
van der Gucht, J., Davelaar, J., Hendriks, L., Porth, O., Olivares, H., Mizuno, Y., Fromm, C. M., & Falcke, H. (2020) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201937014 · Cited by 33 · see citing works
GIGA-Lens: Fast Bayesian Inference for Strong Gravitational Lens Modeling
Gu, A., Huang, X., Sheu, W., Aldering, G., Bolton, A. S., Boone, K., Dey, A. T., Filipp, A., Jullo, E., Perlmutter, S., et al. (2022) · The Astrophysical Journal · doi:10.3847/1538-4357/ac6de4 · Cited by 33 · see citing works
“The introduction of neural networks to identify gravitational lens candidates in imaging surveys has been transformational (e.g., Jacobs et al. 2017; Metcalf et al. 2019; Jacobs et al. 2019a, 2019b; Cañameras et al. 2020).”
BackgroundWilde, J., Serjeant, S., Bromley, J. M., Dickinson, H., Koopmans, L. V. E., & Metcalf, R. B. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac562 · Cited by 31 · see citing works
Tran, K., Harshan, A., Glazebrook, K., C., K. V. G., Jones, T. A., Jacobs, C., Kacprzak, G. G., Barone, T. M., Collett, T. E., Gupta, A., et al. (2022) · The Astronomical Journal · doi:10.3847/1538-3881/ac7da2 · Cited by 31 · see citing works
“We build on earlier searches that used human inspection (More et al. 2016; Diehl et al. 2017), lens modeling (Chan et al. 2015), or neural networks (Jacobs et al. 2017; Petrillo et al. 2017; Huang et al. 2020) to identify high-quality gravitational lenses and increase the number of candidates from…”
MethodSelf-citationNew Strong Gravitational Lenses from the DESI Legacy Imaging Surveys Data Release 9
Storfer, C., Huang, X., Gu, A., Sheu, W., Banka, S., Dey, A. T., Reyes, J. I., Jain, A. K., V, Kwon, K. J., Lang, D., et al. (2024) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ad527e · Cited by 30 · see citing works
“…also have been carried out in the Hyper-Suprime Cam Subaru Strategic Program (HSC SSP), Canada–France–Hawaii Telescope Legacy Survey (CFHTLS), and the Kilo-Degree Survey (KiDS), all of which are contained within the Legacy Surveys (e.g., Jacobs et al. 2017; Cañameras et al. 2021; Li et al. 2021).”
BackgroundPearson, J., Maresca, J., Li, N., & Dye, S. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab1547 · Cited by 31 · see citing works
Recovering the CMB Signal with Machine Learning
Wang, G.-J., Shi, H., Yan, Y., Xia, J.-Q., Zhao, Y.-Y., Li, S.-Y., & Li, J.-F. (2022) · Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ac5f4a · Cited by 18 · see citing works
“…and reionization models (Schmelzle et al. 2017; Hassan et al. 2018), searching for and estimating the parameters of strong gravitational lenses (Jacobs et al. 2017; Hezaveh et al. 2017; Petrillo et al. 2017; Pourrahmani et al. 2018; Schaefer et al. 2018), classifying the large-scale structure…”
BackgroundDeep Horizon: A machine learning network that recovers accreting black hole parameters
van der Gucht, J., Davelaar, J., Hendriks, L., Porth, O., Olivares, H., Mizuno, Y., Fromm, C. M., & Falcke, H. D. (2020) · Springer Link (Chiba Institute of Technology) · doi:10.1051/0004-6361/201937014/pdf · Cited by 27 · see citing works
Extracting cosmological parameters from N-body simulations using machine learning techniques
Lazanu, A. (2021) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2021/09/039 · Cited by 23 · see citing works
“…been used in a variety of cosmology setups: CMB [8, 9], LSS [10–13], reionization and 21cm [14, 15], gravitational lensing: weak lensing [16, 17], strong lensing [18, 19], redshift prediction [20, 21], parameter estimation [22, 23], and are expected to provide more insights in the future [24].”
Searching for Strong Gravitational Lenses
Lemon, C., Courbin, F., More, A., Schechter, P. L., Cañameras, R., Delchambre, L., Leung, C., Shu, Y., Spiniello, C., Hezaveh, Y., et al. (2024) · Space Science Reviews · doi:10.1007/s11214-024-01042-9 · Cited by 25 · see citing works
“Machine learning’s first successful applications for lens finding were as late as 2017 (Petrillo et al. 2017; Jacobs et al. 2017; Lanusse et al. 2018), partly due to the advent of wide-field, deep surveys, but also to the requirement of large computational resources associated with the processing of…”
Self-citationRezaei, S., McKean, J. P., Biehl, M. L., de Roo, W., & Lafontaine, A. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac2078 · Cited by 24 · see citing works
Gentile, F., Tortora, C., Covone, G., Koopmans, L. V. E., Spiniello, C., Fan, Z., Li, R., Liu, D., Napolitano, N. R., Vaccari, M., et al. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab3386 · Cited by 1 · see citing works
“…as the Kilo-Degree Survey (KiDS, Petrillo et al. 2017, 2019a,b; Li et al. 2020), theDark Energy Survey (DES, Jacobs et al. 2019a,b) the Pan-STARRS survey (Canameras et al. 2020), the DESI survey (Huang et al. 2020), and the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS, Jacobs et al. 2017).”
MethodProspects for recovering galaxy intrinsic shapes from projected quantities
Bassett, R., & Foster, C. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz1440 · Cited by 23 · see citing works
“…that machine learning techniques are routinely applied to astronomical imaging data to solve a variety of problems including morphological classifications (e.g. Domı́nguez Sánchez et al. 2018; Lukic et al. 2018) and selections of lensed galaxies (e.g. Jacobs et al. 2017; Pourrahmani et al. 2018).”
BackgroundThe impact of human expert visual inspection on the discovery of strong gravitational lenses
Rojas, K., Collett, T. E., Ballard, D., Magee, M. R., Birrer, S., Buckley-Geer, E., Chan, J. H. H., Clément, B., Diego, J. M., Gentile, F., et al. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad1680 · Cited by 22 · see citing works
“As astronomy enters the era of billion object surveys, sophisticated methods for discovering strong lenses have been developed (e.g. Lanusse et al. 2018; Avestruz et al. 2019) and applied (Jacobs et al. 2017, 2019b,a; Rojas et al. 2022; Savary et al. 2022; Petrillo et al. 2017, 2019).”
MethodSelf-citationIdentification of BASS DR3 sources as stars, galaxies, and quasars by XGBoost
Li, C., Zhang, Y., Cui, C., Fan, D., Zhao, Y., Wu, X.-B., He, B., Xu, Y., Li, S., Han, J., et al. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab1650 · Cited by 22 · see citing works
Automated Lensing Learner: Automated Strong Lensing Identification with a Computer Vision Technique
Avestruz, C., Li, N., Zhu, H., Lightman, M., Collett, T. E., & Luo, W. (2019) · The Astrophysical Journal · doi:10.3847/1538-4357/ab16d9 · Cited by 21 · see citing works
“For comparison, the precision-recall values quoted in other simulation-based tests are 94%-100% in precision with 96%- 100% recall in Jacobs et al. (2017) using convolutional neural networks, where the lens to non-lens ratio is approximately 1:1.”
MethodInfluentialSelf-citationSadeghi, M., Javaherian, M., & Miraghaei, H. (2021) · The Astronomical Journal · doi:10.3847/1538-3881/abd314 · Cited by 20 · see citing works
Likelihood-free Inference with the Mixture Density Network
Wang, G.-J., Cheng, C., Ma, Y.-Z., & Xia, J. (2022) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ac7da1 · Cited by 20 · see citing works
“For example, it performs excellently in searching for and estimating the parameters of strong gravitational lenses (Jacobs et al. 2017; Hezaveh et al. 2017; Petrillo et al. 2017; Pourrahmani et al. 2018; Schaefer et al. 2018), an- † Corresponding author: ma@ukzn.ac.za alyzing gravitational waves…”
BackgroundMachine-learning Classifiers for Intermediate Redshift Emission-line Galaxies
Zhang, K., Schlegel, D. J., Andrews, B. H., Comparat, J., Schäfer, C., Vázquez-Mata, J. A., Kneib, J., & Yan, R. (2019) · The Astrophysical Journal · doi:10.3847/1538-4357/ab397e · Cited by 20 · see citing works
“…neural networks are also well-suited for identifying strong lens systems in galaxy images because these systems can be robustly simulated even though they are rare in nature (Jacobs et al. 2017, 2019a, 2019b; Petrillo et al. 2017; Metcalf et al. 2018; Pourrahmani et al. 2018; Huang et al. 2019).”
BackgroundZaborowski, E. A., Drlica-Wagner, A., Ashmead, F., Wu, J. F., Morgan, R., Bom, C. R., Shajib, A. J., Birrer, S., Cerny, W., Buckley‐Geer, E., et al. (2023) · The Astrophysical Journal · doi:10.3847/1538-4357/ace4ba · Cited by 19 · see citing works
Self-citationJaelani, A. T., More, A., Wong, K. C., Inoue, K. T., Chao, D. C.-Y., Premadi, P. W., & Cañameras, R. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae2442 · Cited by 19 · see citing works
Self-citationStapelberg, S., Carrasco, M., & Maturi, M. (2018) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/sty2784 · Cited by 19 · see citing works
“In order to alleviate the human bottleneck in lens detection, increasing interest has lately been devoted to machine learning methods (e.g. Bom et al. 2017; Jacobs et al. 2017).”
MethodCañameras, R., Schuldt, S., Shu, Y., Suyu, S. H., Taubenberger, S., Andika, I. T., Bag, S., Inoue, K. T., Jaelani, A. T., Leal-Taixé, L., et al. (2024) · Astronomy & Astrophysics · doi:10.1051/0004-6361/202347072 · Cited by 19 · see citing works
Self-citationMachine-learning classifiers for intermediate redshift emission-line galaxies
Zhang, K., Schlegel, D. J., Andrews, B. H., Comparat, J., Schäfer, C., Vázquez-Mata, J. A., Kneib, J., & Yan, R. (2019) · MPG.PuRe (Max Planck Society) · Cited by 18 · see citing works
Finding strong gravitational lenses through self-attention
Thuruthipilly, H., Zadrożny, A., Pollo, A., & Biesiada, M. (2022) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202142463 · Cited by 17 · see citing works
Schuldt, S., Cañameras, R., Shu, Y., Suyu, S. H., Taubenberger, S., Meinhardt, T., & Leal-Taixé, L. (2023) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202244325 · Cited by 17 · see citing works
On the detectability of strong lensing in near-infrared surveys
Holloway, P. H., Verma, A., Marshall, P. J., More, A., & Tecza, M. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad2371 · Cited by 16 · see citing works
Self-citationDeep Learning assessment of galaxy morphology in S-PLUS Data Release 1
Bom, C., Cortesi, A., Lucatelli, G., Dias, L., Schubert, P., Schwarz, G. B. O., Cardoso, N. M., Lima, E. V., de Oliveira, C. D., Sodré, L., et al. (2021) · Monthly notices of the Royal Astronomical Society · doi:10.1093/mnras/stab1981 · Cited by 6 · see citing works
Schuldt, S., Suyu, S. H., Cañameras, R., Shu, Y., Taubenberger, S., Ertl, S., & Halkola, A. (2023) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202244534 · Cited by 16 · see citing works
Jacobs, C., Collett, T. E., Glazebrook, K., Buckley‐Geer, E., Diehl, H. T., Lin, H., McCarthy, C., Qin, A., Odden, C., Escudero, M., et al. (2019) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ab26b6 · Cited by 15 · see citing works
Self-citationDetection of Strongly Lensed Arcs in Galaxy Clusters with Transformers
Jia, P., Sun, R., Li, N., Song, Y., Ning, R., Wei, H., & Luo, R. (2022) · The Astronomical Journal · doi:10.3847/1538-3881/aca1c2 · Cited by 15 · see citing works
“In particular, detecting the galaxy-scale strong-lensing systems has been remarkably successful (Bom et al. 2017; Ostrovski et al. 2017; Jacobs et al. 2017; Hartley et al. 2017; Petrillo et al. 2017; Lanusse et al. 2018; Avestruz et al. 2019), and the number of high-quality candidates of…”
BackgroundLeuzzi, L., Meneghetti, M., Angora, G., Metcalf, R. B., Moscardini, L., Rosati, P., Bergamini, P., Calura, F., Clément, B., Gavazzi, R., et al. (2023) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202347244 · Cited by 15 · see citing works
O’Donnell, J. H., Wilkinson, R. D., Diehl, H. T., Aros-Bunster, C., Bechtol, K., Birrer, S., Buckley‐Geer, E., Rosell, A. C., Kind, M. C., da Costa, L. N., et al. (2022) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ac470b · Cited by 15 · see citing works
“CFHTLS imaging yielded 16 more probable to definite SL systems using neural-network-based searches (Jacobs et al. 2017).”
MethodTeimoorinia, H., Toyonaga, R. D., Fabbro, S., & Bottrell, C. (2020) · Publications of the Astronomical Society of the Pacific · doi:10.1088/1538-3873/ab747b · Cited by 15 · see citing works
“A common method of solving the problem of limited real training data is to simulate gravitational lenses using ray-tracing software (Jacobs et al. 2017; Petrillo et al. 2017; Pourrahmani et al. 2018).”
MethodEuclid Collaboration, Lines, N. E. P., Collett, T. E., Walmsley, M., Rojas, K., Li, T., Leuzzi, L., Manjón-García, A., Vincken, S. H., Wilde, J., et al. (2026) · Astronomy & Astrophysics · doi:10.1051/0004-6361/202554542 · Cited by 14 · see citing works
Self-citationA model for galaxy–galaxy strong lensing statistics in surveys
Ferrami, G., & Wyithe, J. S. B. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae1607 · Cited by 14 · see citing works
SLICK: Strong Lensing Identification of Candidates Kindred in gravitational wave data
Magare, S., More, A., & Choudhary, S. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae2408 · Cited by 14 · see citing works
Self-citationAuto-detection of strong gravitational lenses using convolutional neural networks
Pearson, J. F., Pennock, C. M., & Robinson, T. (2018) · Emergent Scientist · doi:10.1051/emsci/2017010 · Cited by 12 · see citing works
“A similar case emerged in Jacobs et al. (2017) [31], where the simulated images used were produced using one of two methods.”
MethodAssessment of Astronomical Images Using Combined Machine-learning Models
Teimoorinia, H., Kavelaars, J. J., Gwyn, S., Durand, D., Rolston, K., & Ouellette, A. (2020) · The Astronomical Journal · doi:10.3847/1538-3881/ab7938 · Cited by 14 · see citing works
“They can predict different physical parameters and detect different patterns in different surveys (Rafieferantsoa et al. 2018) or recognize various interesting astronomical sources, such as strong lensing candidates (Jacobs et al. 2017; Pourrahmani et al. 2018; Lanusse et al. 2018).”
BackgroundGalaxy Morphology Classification via Deep Semisupervised Learning with Limited Labeled Data
Luo, Z., Chen, J., Chen, Z., Zhang, S., Fu, L., Xiao, H., & Shu, C. (2025) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/addb4c · Cited by 13 · see citing works
“Research indicates that models trained on synthetic data often require fine-tuning with real data prior to deployment to address these distribution discrepancies (Jacobs et al. 2017, 2019; Pearson et al. 2019; ´Ciprijanovi´c et al. 2020; He et al. 2020; Krishnakumar & Kalmbach 2024).”
Bom, C. R., Cortesi, A., Ribeiro, U., Dias, L. O., Kelkar, K., Castelli, A. V. S., Santana-Silva, L., Lopes-Silva, V. H., Gonçalves, T. S., Abramo, L. R., et al. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad3956 · Cited by 12 · see citing works
Systematic comparison of neural networks used in discovering strong gravitational lenses
More, A., Cañameras, R., Jaelani, A. T., Shu, Y., Ishida, Y., Wong, K. C., Inoue, K. T., Schuldt, S., & Sonnenfeld, A. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae1597 · Cited by 12 · see citing works
Self-citationNot hydro: using neural networks to estimate galaxy properties on a dark-matter-only simulation
Hernández, C. A., González, R., & Padilla, N. D. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad2112 · Cited by 11 · see citing works
A Comprehensive Guide to Interpretable AI-Powered Discoveries in Astronomy
Lieu, M. (2025) · Universe · doi:10.3390/universe11060187 · Cited by 13 · see citing works
“This scalability challenge spurred the adoption of convolutional neural networks (CNNs), which automatically learn hierarchical features from raw pixels (e.g., [48,49]).”
Cheng, T.-Y., Sánchez, H. D., Vega-Ferrero, J., Conselice, C. J., Siudek, M., Aragón‐Salamanca, A., Bernardi, M., Cooke, R., Ferreira, L., Krywult, J., et al. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac3228 · Cited by 11 · see citing works
A Bayesian approach to strong lens finding in the era of wide-area surveys
Holloway, P., Marshall, P. J., Verma, A., More, A., Cañameras, R., Jaelani, A. T., Ishida, Y., & Wong, K. C. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae875 · Cited by 11 · see citing works
Self-citationTiming the last major merger of galaxy clusters with large halo sparsity
Richardson, T. R. G., & Corasaniti, P.-S. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac1241 · Cited by 11 · see citing works
Sarkar, S., Narayanan, G., Banerjee, A., & Prakash, P. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac3096 · Cited by 11 · see citing works
Bharath Chowdhary, N., Koopmans, L. V. E., Valentijn, E. A., Kleijn, G. V., de Jong, J. T. A., Napolitano, N. R., Li, R., Tortora, C., Busillo, V., & Dong, Y. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae1882 · Cited by 10 · see citing works
Uncovering tidal treasures: automated classification of faint tidal features in DECaLS data
Gordon, A. J., Ferguson, A. M. N., & Mann, R. G. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae2169 · Cited by 10 · see citing works
Retrospective Search for Strongly Lensed Supernovae in the DESI Legacy Imaging Surveys
Sheu, W., Huang, X., Cikota, A., Suzuki, N., Schlegel, D. J., & Storfer, C. (2023) · The Astrophysical Journal · doi:10.3847/1538-4357/acd1e4 · Cited by 10 · see citing works
“…found within DECaLS, with the majority from Huang et al. 2020, Huang et al. 2021, and Storfer et al. 2022, and the rest from Moustakas 2012, Carrasco et al. 2017, Diehl et al. 2017, Jacobs et al. 2017, Pourrahmani et al. 2018, Sonnenfeld & Leauthaud 2018, Wong et al. 2018, and Jacobs et al. 2019.”
BackgroundImage Simulations for Strong and Weak Gravitational Lensing
Plazas, A. A. (2020) · Symmetry · doi:10.3390/sym12040494 · Cited by 10 · see citing works
Bom, C. R., Poh, J., Nord, B., Valentín, M. B., & Dias, L. O. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.06341 · Cited by 10 · see citing works
Schuldt, S., Cañameras, R., Andika, I. T., Bag, S., Melo, A., Shu, Y., Suyu, S. H., Taubenberger, S., & Grillo, C. (2024) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202450927 · Cited by 9 · see citing works
Developing a victorious strategy to the second strong gravitational lensing data challenge
Bom, C. R., Fraga, B. M. O., Dias, L. O., Schubert, P., Valentin, M. B., Furlanetto, C., Makler, M., Teles, K., de Albuquerque, M. P., & Metcalf, R. B. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac2047
Li, X., Sun, R., Lv, J., Jia, P., Li, N., Wei, C., Zou, H., Er, X., Chen, Y., Ban, Z., et al. (2024) · The Astronomical Journal · doi:10.3847/1538-3881/ad395e · Cited by 9 · see citing works
“Within the realm of galaxy evolution studies, Schawinski et al. (2017) have proposed deep generative models, notably generative adversarial networks (GANs) (Goodfellow et al. 2014; Jacobs et al. 2017), and devised an innovative deconvolution technique to recover features from SDSS galaxy images.”
BackgroundMethodInfluentialA Modular Deep Learning Pipeline for Galaxy-Scale Strong Gravitational Lens Detection and ModelingPreprint
Madireddy, S., Ramachandra, N., Li, N., Butler, J. W., Balaprakash, P., Habib, S., Heitmann, K., & Collaboration, T. L. D. E. S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.03867 · Cited by 9 · see citing works
“For instance, the strong gravitational lens detection challenge [33] proved the relative success of applying various ML techniques for automated detection of GGSL systems [6, 9, 19, 23, 30, 36, 41] in comparison with traditional feature extraction techniques.”
MethodEuclid Quick Data Release (Q1)
Walmsley, M., Holloway, P., Lines, N. E. P., Rojas, K., Collett, T. E., Verma, A., Li, T., Nightingale, J. W., Despali, G., Schuldt, S., et al. (2026) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202555141 · Cited by 8 · see citing works
Self-citationMagro, D., Adami, K. Z., DeMarco, A., Riggi, S., & Sciacca, . (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab1635 · Cited by 8 · see citing works
Jacobs, C., Glazebrook, K., Qin, A. K., & Collett, T. E. (2021) · Astronomy and Computing · doi:10.1016/j.ascom.2021.100535 · Cited by 8 · see citing works
“CNNs have now been successfully employed to discover new lenses in surveys such as the Canada-France-Hawaii Legacy Survey (Jacobs et al., 2017), Dark Energy Survey (Jacobs et al., 2019a,b), Hyper Suprime-Cam Subaru Strategic Program (Sonnenfeld et al., 2018) and Kilo-Degree Survey (Petrillo et al., 2019).”
MethodSelf-citationNagam, B. C., Acevedo Barroso, J. A., Wilde, J., Andika, I. T., Manjón-García, A., Pearce-Casey, R., Stern, D., Nightingale, J. W., Moustakas, L. A., McCarthy, K., et al. (2025) · Astronomy & Astrophysics · doi:10.1051/0004-6361/202554132 · Cited by 8 · see citing works
Self-citationEuclid Quick Data Release (Q1)
Rojas, K., Collett, T. E., Barroso, J. A. A., Nightingale, J. W., Stern, D. K., Moustakas, L. A., Schuldt, S., Despali, G., Melo, A., Walmsley, M., et al. (2025) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202554605 · Cited by 7 · see citing works
Self-citationDenzel, P., Mukherjee, S., Coles, J., & Saha, P. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa108 · Cited by 7 · see citing works
“In the verification of lens candidates, machine-learning based projects and citizen science programs have been established, working concurrently, as well as complementary, to confirm and select interesting objects (Lanusse et al. 2017; Jacobs et al. 2017; Marshall et al. 2016; More et al. 2016).”
BackgroundYOLOX-LS: Strong Gravitational Lenses Detection in the DECaLS with Deep Learning
Liu, Y. T., Tu, L., Li, J., Miao, J., Lin, G., & Zhao, C. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/ad97ba · Cited by 5 · see citing works
Popov, A. A., Строков, В. Н., & Surdyaev, A. A. (2021) · Astronomy and Computing · doi:10.1016/j.ascom.2021.100467 · Cited by 5 · see citing works
“…in astrophysics: to name a few, automated analysis and detection of strong gravitational lenses (Hezaveh et al., 2017; Petrillo et al., 2017; Jacobs et al., 2017; Lanusse et al., 2017; Pourrahmani et al., 2018; Schaefer et al., 2018), dark matter halo simulations (Agarwal et al., 2018;…”
MethodThe AGEL Survey Data Release 2: A Gravitational Lens Sample for Galaxy Evolution and Cosmology
Barone, T. M., C., K. V. G., Tran, K., Kacprzak, G. G., Glazebrook, K., Jones, T. A., Bowden, D. J., Dalessandro, F., Sahu, N., Skobe, H., et al. (2025) · The Astronomical Journal · doi:10.3847/1538-3881/ae1f86 · Cited by 4 · see citing works
“CNNs have been successfully applied to the Canada–France–Hawaii Telescope Legacy Survey ( CFHTLS; C. Jacobs et al. 2017 ) , the Kilo-Degree Survey ( KiDS; C. E. Petrillo et al. 2017, 2019; R. Li et al. 2020 ) , Dark Energy Survey ( DES; C. Jacobs et al. 2019a, 2019b ) , Panoramic Survey Telescope &…”
Self-citation
- Authors
- Jacobs, C., Glazebrook, K., Collett, T., More, A., & McCarthy, C.
- Affiliations
- Swinburne University of Technology (AU)
- University of Portsmouth (GB)
- Kavli Institute for the Physics and Mathematics of the Universe (JP)
- Corresponding author
- Jacobs, C.
- Venue
- Monthly Notices of the Royal Astronomical Society
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-06-15
- Volume
- 471
- Issue
- 1
- Pages
- 167-181
- ISSN
- 0035-8711 (ISSN-L)
- 1365-2966
- OpenAlex
- W2606720601
- Cited by
- 133
- References
- 83
- Citations by year
- 2026: 6 · 2025: 16 · 2024: 18 · 2023: 14 · 2022: 18 · 2021: 14 · 2020: 16 · 2019: 18 · 2018: 9 · 2017: 4
- FWCI
- 7.03
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Monthly Notices of the Royal Astronomical Society (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Swinburne figshare (Swinburne University of Technology) (submittedVersion)
- arXiv (Cornell University)
- Swinburne figshare (Swinburne University of Technology)
- 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 › Engineering › Computational Mechanics › Astronomical Observations and Instrumentation
- Keywords
- strong gravitational lensing, lens candidate selection, convolutional neural networks, CFHTLS, Dark Energy Survey, LSST
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
