CoChat
Sign Up
Cited by 18Galaxies: Formation, Evolution, PhenomenaAstrophysical Phenomena and ObservationsAdvanced Neural Network Applications

Euclid : Searches for strong gravitational lenses using convolutional neural nets in Early Release Observations of the Perseus field

Open access
Pearce-Casey, R., Nagam, B. C., Wilde, J., Busillo, V., Ulivi, L., Andika, I. T., Manjón-García, A., Leuzzi, L. +199 more·2025·Astronomy & Astrophysics
DOI 10.1051/0004-6361/202453152
Open this paper in CoChat — free accountOpen PDF

Abstract

The Euclid Wide Survey (EWS) is predicted to find approximately 170 000 galaxy-galaxy strong lenses from its lifetime observation of 14 000 deg 2 of the sky. Detecting this many lenses by visual inspection with professional astronomers and citizen scientists alone is infeasible. As a result, machine learning algorithms, particularly convolutional neural networks (CNNs), have been used as an automated method of detecting strong lenses, and have proven fruitful in finding galaxy-galaxy strong lens candidates, such that the usage of CNNs in lens identification has increased. We identify the major challenge to be the automatic detection of galaxy-galaxy strong lenses while simultaneously maintaining a low false positive rate, thus producing a pure and complete sample of strong lens candidates from Euclid with a limited need for visual inspection. One aim of this research is to have a quantified starting point on the achieved purity and completeness with our current version of CNN-based detection pipelines for the VIS images of EWS. This work is vital in preparing our CNN-based detection pipelines to be able to produce a pure sample of the >100 000 strong gravitational lensing systems widely predicted for Euclid . We select all sources with VIS I E < 23 mag from the Euclid Early Release Observation imaging of the Perseus field. We apply a range of CNN architectures to detect strong lenses in these cutouts. All our networks perform extremely well on simulated data sets and their respective validation sets. However, when applied to real Euclid imaging, the highest lens purity is just ∼11%. Among all our networks, the false positives are typically identifiable by human volunteers as, for example, spiral galaxies, multiple sources, and artifacts, implying that improvements are still possible, perhaps via a second, more interpretable lens selection filtering stage. There is currently no alternative to human classification of CNN-selected lens candidates. Given the expected ∼10 5 lensing systems in Euclid , this implies 10 6 objects for human classification, which while very large is not in principle intractable and not without precedent.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1051/0004-6361/202453152

Cites

78 of 78 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The low purity in the Euclid ERO lens-finding experiment may be partly due to the number of fine-tuning layers being set to one and not allowed to vary.

    “When Zoobot was applied to the Euclid EROdata in PearceCasey et al. (2025), Zoobot was found to perform better than the other models tested. However, they found that the purity of the lenses discovered was low, which may be partly due to the fact that the number of fine-tuning layers was set to one and was not allowed to vary.”

    From Euclid Quick Data Release (Q1): XXVIII. The Strong Lensing Discovery Engine C – Finding lenses with machine learning (2026) · cites this paper as Pearce-Casey et al. (2025)

    The Discussion and conclusion reports low purity: even the best model produced roughly nine false positives per true positive. Section 3.1 says Zoobot was fine-tuned with its original layers frozen and a new, two-dense-layer head, but the paper does not say that the number of fine-tuning layers was set to one or link that choice to low purity. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • When Zoobot was applied to Euclid ERO data in Pearce-Casey et al. (2025), Zoobot performed better than the other models tested, but the purity of lenses discovered was low.

    “When Zoobot was applied to the Euclid EROdata in PearceCasey et al. (2025), Zoobot was found to perform better than the other models tested. However, they found that the purity of the lenses discovered was low, which may be partly due to the fact that the number of fine-tuning layers was set to one and was not allowed to vary.”

    From Euclid Quick Data Release (Q1): XXVIII. The Strong Lensing Discovery Engine C – Finding lenses with machine learning (2026) · cites this paper as Pearce-Casey et al. (2025)

  • Zoobot was found to be the best-performing machine-learning model of the 20 models tested on Euclid ERO data, motivating its use for lens finding in Q1.

    “The idea of using Zoobot as a base model for lens finding was first presented and tested by Pearce-Casey et al. (2025), where it was found to be the best-performing machinelearning model of the 20 models tested on Euclid ERO data; this motivated the use of Zoobot for lens finding in Q1.”

    From Euclid Quick Data Release (Q1): XXVIII. The Strong Lensing Discovery Engine C – Finding lenses with machine learning (2026) · cites this paper as Pearce-Casey et al. (2025)

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

Flagged first, then by citations

Research works printed but not linked: 105

  1. Acevedo Barroso, J. A., O'Riordan, C. M., & Clément, B., e. a. 2025, A&A, 697, A14 · DOI
  2. Amara, A., de la Bella, L., Birrer, S., et al. 2021, J. Open Source Softw., 6, 3056 · DOI
  3. Andika, I. T., Jahnke, K., van der Wel, A., et al. 2023a, ApJ, 943, 150 · DOI
  4. Andika, I. T., Suyu, S. H., Cañameras, R., et al. 2023b, A&A, 678, A103 · DOI
  5. Astropy Collaboration (Robitaille, T. P., et al.) 2013, A&A, 558, A33 · DOI
  6. Astropy Collaboration (Price-Whelan, A. M., et al.) 2018, AJ, 156, 123 · DOI
  7. Astropy Collaboration (Price-Whelan, A. M., et al.) 2022, ApJ, 935, 167 · DOI
  8. Auger, M., Treu, T., Bolton, A., et al. 2009, ApJ, 705, 1099 · DOI
  9. Birrer, S., & Amara, A. 2018, Phys. Dark Universe, 22, 189 · DOI
  10. Bolton, A. S., Burles, S., Koopmans, L. V. E., et al. 2008, ApJ, 682, 964 · DOI
  11. Buscema, M. 1998, Substance Use Misuse, 33, 233 · DOI
  12. Caminha, G. B., Suyu, S. H., Grillo, C., & Rosati, P. 2022, A&A, 657, A83 · DOI
  13. Cañameras, R., Schuldt, S., Suyu, S., et al. 2020, A&A, 644, A163 · DOI
  14. Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. 2020a, in Proceedings of Machine Learning Research, 119, Proceedings of the 37th International Conference on Machine Learning, eds. H. D. III, & A. Singh (PMLR), 1597 · DOI
  15. Chen, T., Kornblith, S., Swersky, K., Norouzi, M., & Hinton, G. E. 2020b, in Advances in Neural Information Processing Systems, 33, eds. H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, & H. Lin (Curran Associates, Inc.), 22243 · DOI
  16. Christ, C., Nord, B., Gozman, K., & Ottenbreit, K. 2020, in American Astronom- ical Society Meeting Abstracts, 235, 303.03 · DOI
  17. Collett, T. E. 2015, ApJ, 811, 20 A214, page 14 of 22 · DOI
  18. Pearce-Casey, R., et al.: A&A, 696, A214 (2025)
  19. Collett, T. E., & Auger, M. W. 2014, MNRAS, 443, 969 · DOI
  20. Cropper, M., Pottinger, S., Niemi, S., et al. 2016, in Space Telescopes and Instru- mentation 2016: Optical, Infrared, and Millimeter Wave, 9904, eds. H. A. MacEwen, G. G. Fazio, M. Lystrup, N. Batalha, N. Siegler, & E. C. Tong (SPIE) Cuillandre, J.-C., Bertin, E., Bolzonella, M., et al. 2025a, A&A, 697, A6 · DOI
  21. Cuillandre, J. C., Bolzonella, M., Boselli, A., et al. 2025b, A&A, 697, A11 · DOI
  22. Davies, A., Serjeant, S., & Bromley, J. M. 2019, MNRAS, 487, 5263 · DOI
  23. de Jong, J. T., Verdoes Kleijn, G. A., Kuijken, K. H., & Valentijn, E. A. 2013, Exp. Astron., 35, 25 · DOI
  24. Denzel, P., Mukherjee, S., & Saha, P. 2021, MNRAS, 506, 1815 · DOI
  25. Domínguez Sánchez, H., Huertas-Company, M., Bernardi, M., Tuccillo, D., & Fischer, J. L. 2018, MNRAS, 476, 3661 · DOI
  26. Euclid Collaboration (Schirmer, M., et al.) 2022, A&A, 662, A92 · DOI
  27. Euclid Collaboration (Ilić, S., et al.) 2022, A&A, 657, A91 · DOI
  28. Euclid Collaboration (Scaramella, R., et al.) 2022, A&A, 662, A112 · DOI
  29. Euclid Collaboration (Aussel, B., et al.) 2024, A&A, 689, A274 · DOI
  30. Euclid Collaboration (Leuzzi, L., et al.) 2024, A&A, 681, A68 · DOI
  31. Euclid Collaboration (Castander, F., et al.) 2025, A&A, 697, A5 · DOI
  32. Euclid Collaboration (Cropper, M., et al.) 2025, A&A, 697, A2 · DOI
  33. Euclid Collaboration (Jahnke, K., et al.) 2025, A&A, 697, A3 · DOI
  34. Euclid Collaboration (Mellier, Y., et al.) 2025, A&A, 697, A1 European Space Agency 2024, Euclid Early Release Observations, https:// doi.org/10.57780/esa-qmocze3 · DOI
  35. Faber, S., & Jackson, R. E. 1976, ApJ, 204, 668 · DOI
  36. Gentile, F., Tortora, C., Covone, G., et al. 2022, MNRAS, 510, 500 · DOI
  37. Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357 · DOI
  38. Hayat, M. A., Stein, G., Harrington, P., Lukić, Z., & Mustafa, M. 2021, ApJ, 911, L33 · DOI
  39. He, K., Zhang, X., Ren, S., & Sun, J. 2016, in Proceedings of the IEEE conference on computer vision and pattern recognition, 770 · DOI
  40. He, K., Gkioxari, G., Dollár, P., & Girshick, R. 2017, in 2017 IEEE International Conference on Computer Vision (ICCV), 2980 · DOI
  41. He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. 2020, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · DOI
  42. Hecht-Nielsen, R. 1992, in Neural Networks for Perception (Elsevier), 65 · DOI
  43. Howard, A., Sandler, M., Chu, G., et al. 2019, in Proceedings of the IEEE/CVF International Conference on Computer Vision, 1314 · DOI
  44. Hu, J., Shen, L., & Sun, G. 2018, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 7132 · DOI
  45. Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. 2017, in Proceed- ings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 4700 · DOI
  46. Huertas-Company, M., Guo, Y., Ginzburg, O., et al. 2020, MNRAS, 499, 814 · DOI
  47. Huertas-Company, M., Sarmiento, R., & Knapen, J. H. 2023, RAS Tech. Instrum., 2, 441 · DOI
  48. Hunter, J. D. 2007, Comput. Sci. Eng., 9, 90 · DOI
  49. Jacobs, C., Glazebrook, K., Qin, A., & Collett, T. 2022, Astron. Comput., 38, 100535 · DOI
  50. Kingma, D. P., & Ba, J. 2015, in 3rd International Conference on Learning Rep- resentations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, eds. Y. Bengio, & Y. LeCun Koopmans, L. V. E., Bolton, A., Treu, T., et al. 2009, ApJ, 703, L51 · DOI
  51. Lanusse, F., Ma, Q., Li, N., et al. 2018, MNRAS, 473, 3895 · DOI
  52. LeCun, Y., Boser, B., Denker, J. S., et al. 1989, Neural Computat., 1, 541 · DOI
  53. Lehar, J., Falco, E. E., Kochanek, C. S., et al. 2000, ApJ, 536, 584 · DOI
  54. Li, R., Shu, Y., & Wang, J. 2018, MNRAS, 480, 431 · DOI
  55. Li, R., Napolitano, N. R., Tortora, C., et al. 2020, ApJ, 899, 30 · DOI
  56. Li, R., Napolitano, N. R., Spiniello, C., et al. 2021, ApJ, 923, 16 · DOI
  57. Li, T., Collett, T. E., Krawczyk, C. M., & Enzi, W. 2024, MNRAS, 527, 5311 · DOI
  58. Limousin, M., Kneib, J.-P., & Natarajan, P. 2005, MNRAS, 356, 309 · DOI
  59. Manjón-García, A. 2021, PhD thesis, University of Cantabria (Spain)
  60. Masters, K. L. 2019, Proc. Int. Astron. Union, 14, 205 · DOI
  61. Meneghetti, Bartelmann, M., Dolag, K., et al. 2005, A&A, 442, 413 · DOI
  62. Metcalf, Meneghetti, M., Avestruz, C., et al. 2019, A&A, 625, A119 · DOI
  63. Miralda-Escude, J. 1991, ApJ, 370, 1 · DOI
  64. Möller, O., Natarajan, P., Kneib, J.-P., & Blain, A. 2002, ApJ, 573, 562 · DOI
  65. More, A., Cañameras, R., Jaelani, A. T., et al. 2024, MNRAS, 533, 525 · DOI
  66. Nagam, B., Koopmans, L. V. E., Valentijn, E. A., et al. 2023, MNRAS, 523, 4188 · DOI
  67. Nagam, B., Koopmans, L. V. E., Valentijn, E. A., et al. 2024, MNRAS, 533, 1426 · DOI
  68. Newton, E. R., Marshall, P. J., Treu, T., et al. 2011, ApJ, 734, 104 · DOI
  69. Nord, B., Buckley-Geer, E., Lin, H., et al. 2016, ApJ, 827, 51 · DOI
  70. O'Riordan, C. M., Oldham, L. J., Nersesian, A., et al. 2025, A&A, 694, A145 · DOI
  71. Petrillo, C. E., Tortora, C., Chatterjee, S., et al. 2017, MNRAS, 472, 1129 · DOI
  72. Petrillo, C. E., Tortora, C., Chatterjee, S., et al. 2019a, MNRAS, 482, 807 · DOI
  73. Petrillo, C. E., Tortora, C., Vernardos, G., et al. 2019b, MNRAS, 484, 3879 · DOI
  74. Pourrahmani, M., Nayyeri, H., & Cooray, A. 2018, ApJ, 856, 68 · DOI
  75. Powell, D. M., Vegetti, S., McKean, J. P., et al. 2023, MNRAS, 524, L84 · DOI
  76. Radosavovic, I., Johnson, J., Xie, S., Lo, W.-Y., & Dollar, P. 2020, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 10428 · DOI
  77. Rezaei, S., McKean, J. P., Biehl, M., de Roo, W., & Lafontaine, A. 2022, MNRAS, 517, 1156 · DOI
  78. Ronneberger, O., Fischer, P., & Brox, T. 2015, in International Conference on Medical image computing and computer-assisted intervention (Springer), 234 · DOI
  79. Schaefer, C., Geiger, M., Kuntzer, T., & Kneib, J.-P. 2018, A&A, 611, A2 · DOI
  80. Scoville, N., Aussel, H., Brusa, M., et al. 2007, ApJS, 172, 1 · DOI
  81. Shu, Y., Brownstein, J. R., Bolton, A. S., et al. 2017, ApJ, 851, 48 · DOI
  82. Simonyan, K., & Zisserman, A. 2015, 3rd International Conference on Learn- ing Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings · DOI
  83. Spiniello, C., Barnabè, M., Koopmans, L. V. E., & Trager, S. C. 2015, MNRAS, 452, L21 · DOI
  84. Stein, G., Blaum, J., Harrington, P., Medan, T., & Lukić, Z. 2022, ApJ, 932, 107 · DOI
  85. Storfer, C., Huang, X., Gu, A., et al. 2022, ApJ, submitted [arXiv:2206:02764]
  86. Suyu, S. H., Marshall, P. J., Auger, M. W., et al. 2010, ApJ, 711, 201 · DOI
  87. Szegedy, C., Wei Liu, Yangqing Jia, et al. 2015, in Proceedings of the IEEE conference on computer vision and pattern recognition, 1 · DOI
  88. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. 2016, in Proceed- ings of the IEEE Conference on Computer Vision and Pattern Recognition, 2818 · DOI
  89. Tan, M., & Le, Q. 2019, in Proceedings of the 36th International Conference on Machine Learning, 97, esd. · DOI
  90. K. Chaudhuri, & R. Salakhutdinov (PMLR), 6105
  91. Tan, M., & Le, Q. 2021, in Proceedings of Machine Learning Research, 139, Proceedings of the 38th International Conference on Machine Learning, eds. · DOI
  92. M. Meila ,& T. Zhang (PMLR), 10096
  93. Teimoorinia, H., Bluck, A. F., & Ellison, S. L. 2016, MNRAS, 457, 2086 · DOI
  94. Thuruthipilly, H., Zadrozny, A., Pollo, A., & Biesiada, M. 2022, A&A, 664, A4 · DOI
  95. Treu, T., & Koopmans, L. V. E. 2002, ApJ, 575, 87 · DOI
  96. Vegetti, S., Birrer, S., Despali, G., et al. 2024, Space Sci. Rev., 220, 58 · DOI
  97. Walmsley, M., Scaife, A. M. M., Lintott, C., et al. 2022, MNRAS, 513, 1581 · DOI
  98. Walmsley, M., Allen, C., Aussel, B., et al. 2023, J. Open Source Softw., 8, 5312 · DOI
  99. Wilde, J., Serjeant, S., Bromley, J. M., et al. 2022, MNRAS, 512, 3464 · DOI
  100. Wilde, J. W. 2023, PhD thesis, The Open University, available from https: //oro.open.ac.uk/94777/
  101. Willett, K. W., Lintott, C. J., Bamford, S. P., et al. 2013, MNRAS, 435, 2835 · DOI
  102. Xie, S., Girshick, R. B., Dollár, P., Tu, Z., & He, K. 2016, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 5987 · DOI
  103. You, K., Kou, Z., Long, M., & Wang, J. 2020, in Neural Information Processing Systems · DOI
  104. Zitrin, A., Rosati, P., Nonino, M., et al. 2012, ApJ, 749, 97 101 · DOI
  105. Pearce-Casey, R., et al.: A&A, 696, A214 (2025)

Cited by

Sorted by citations

Related

Authors
Pearce-Casey, R., Nagam, B. C., Wilde, J., Busillo, V., Ulivi, L., Andika, I. T., Manjón-García, A., Leuzzi, L., Matavulj, P., Serjeant, S., Walmsley, M., Acevedo Barroso, J. A., O’Riordan, C. M., Clément, B., Tortora, C., Collett, T. E., Courbin, F., Gavazzi, R., Metcalf, R. B., Cabanac, R., Courtois, H. M., Crook-Mansour, J., Delchambre, L., Despali, G., Ecker, L. R., Franco, A., Holloway, P., Jahnke, K., Mahler, G., Marchetti, L., Melo, A., Meneghetti, M., Müller, O., Nucita, A. A., Pearson, J., Rojas, K., Scarlata, C., Schuldt, S., Sluse, D., Suyu, S. H., Vaccari, M., Vegetti, S., Verma, A., Vernardos, G., Bolzonella, M., Kluge, M., Saifollahi, T., Schirmer, M., Stone, C., Paulino-Afonso, A., Bazzanini, L., Hogg, N. B., Koopmans, L. V. E., Kruk, S., Mannucci, F., Bromley, J. M., Díaz-Sánchez, A., Dickinson, H. J., Powell, D. M., Bouy, H., Laureijs, R., Altieri, B., Amara, A., Andreon, S., Baccigalupi, C., Baldi, M., Balestra, A., Bardelli, S., Battaglia, P., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Caillat, A., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Casas, S., Castellano, M., Castignani, G., Cavuoti, S., Cimatti, A., Colodro-Conde, C., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Cropper, M., Da Silva, A., Degaudenzi, H., De Lucia, G., Di Giorgio, A. M., Dinis, J., Dubath, F., Dupac, X., Dusini, S., Farina, M., Farrens, S., Faustini, F., Ferriol, S., Frailis, M., Franceschi, E., Galeotta, S., George, K., Gillard, W., Gillis, B., Giocoli, C., Gómez-Alvarez, P., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Hudelot, P., Jhabvala, M., Joachimi, B., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kubik, B., Kümmel, M., Kunz, M., Kurki-Suonio, H., Le Mignant, D., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinelli, M., Martinet, N., Marulli, F., Massey, R., Medinaceli, E., Mei, S., Melchior, M., Mellier, Y., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Nakajima, R., Neissner, C., Nichol, R. C., Niemi, S.-M., Nightingale, J. W., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sakr, Z., Sánchez, A. G., Sapone, D., Sartoris, B., Schneider, P., Schrabback, T., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Skottfelt, J., Stanco, L., Steinwagner, J., Tallada-Crespí, P., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Verdoes Kleijn, G., Veropalumbo, A., Wang, Y., Weller, J., Zamorani, G., Zucca, E., Burigana, C., Calabrese, M., Mora, A., Pöntinen, M., Scottez, V., Viel, M., & Margalef-Bentabol, B.
Affiliations
  • The Open University (GB)
  • University of Groningen (NL)
  • Astronomical Observatory of Capodimonte (IT)
  • University of Trento (IT)
  • Max Planck Institute for Astrophysics (DE)
  • Universidad Politécnica de Cartagena (ES)
  • Osservatorio di Astrofisica e Scienza dello Spazio (IT)
  • FHNW University of Applied Sciences and Arts Northwestern Switzerland (CH)
  • University of Toronto (CA)
  • École Polytechnique Fédérale de Lausanne (CH)
  • University of Portsmouth (GB)
  • Institució Catalana de Recerca i Estudis Avançats (ES)
  • Centre National de la Recherche Scientifique (FR)
  • Lyon 1 Université (FR)
  • University of Cape Town (ZA)
  • University of Liège (BE)
  • Istituto Nazionale di Fisica Nucleare, Sezione di Bologna (IT)
  • Max Planck Institute for Extraterrestrial Physics (DE)
  • University of Salento (IT)
  • University of Oxford (GB)
  • University of Minnesota (US)
  • University of Milan (IT)
  • American Museum of Natural History (US)
  • Max Planck Institute for Astronomy (DE)
  • Université de Montréal (CA)
  • Universidade do Porto (PT)
  • University of Ferrara (IT)
  • European Space Astronomy Centre (ES)
  • Arcetri Astrophysical Observatory (IT)
  • University of Surrey (GB)
  • Brera Astronomical Observatory (IT)
  • Scuola Internazionale Superiore di Studi Avanzati (IT)
  • Osservatorio Astronomico di Padova (IT)
  • Osservatorio Astrofisico di Torino (IT)
  • Istituto Nazionale di Fisica Nucleare, Sezione di Torino (IT)
  • National Institute for Astrophysics (IT)
  • Port d'Informació Científica (ES)
  • Astronomical Observatory of Rome (IT)
  • University of Bologna (IT)
  • Instituto de Astrofísica de Canarias (ES)
  • Royal Observatory (GB)
  • University of Manchester (GB)
  • European Space Agency (FR)
  • University College London (GB)
  • University of Geneva (CH)
  • University of Lisbon (PT)
  • Istituto Nazionale di Fisica Nucleare, Sezione di Padova (IT)
  • Institute for Space Astrophysics and Planetology (IT)
  • Trieste Astronomical Observatory (IT)
  • Ludwig-Maximilians-Universität München (DE)
  • University of Oslo (NO)
  • Jet Propulsion Laboratory (US)
  • Lancaster University (GB)
  • Technical University of Denmark (DK)
  • Goddard Space Flight Center (US)
  • University of Helsinki (FI)
  • Netherlands Institute for Radio Astronomy (NL)
  • University of Bonn (DE)
  • Durham University (GB)
  • Institute for High Energy Physics (ES)
  • Newcastle University (GB)
  • University of Copenhagen (DK)
  • University of Waterloo (CA)
  • Agenzia Spaziale Italiana (IT)
  • Centre National d'Études Spatiales (FR)
  • Institute of Space Science - INFLPR Subsidiary (RO)
  • University of Padua (IT)
  • University of Chile (CL)
  • Universität Innsbruck (AT)
  • Institute of Space Sciences (ES)
  • California Institute of Technology (US)
  • Istituto di Radioastronomia di Bologna (IT)
  • Osservatorio Astronomico della Regione Autonoma Valle d'Aosta (IT)
  • Université Catholique de Lille (FR)
  • Space Research Organisation Netherlands (NL)
ORCID
Venue
Astronomy & Astrophysics
Publisher
EDP Sciences
Published
2025-04
Volume
696
Pages
A214
ISSN
  • 0004-6361 (ISSN-L)
  • 1432-0746
DOI
10.1051/0004-6361/202453152
OpenAlex
W4408904176
Cited by
18
References
78
Citations by year
2026: 11 · 2025: 7
FWCI
7.99
Type
journal article
Language
en
Open access
Licence
Locations
  • Astronomy and Astrophysics (publishedVersion, cc-by)
  • Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
  • DIGITAL.CSIC (Spanish National Research Council (CSIC)) (acceptedVersion, other-oa)
  • Open Research Online (The Open University) (acceptedVersion)
  • HAL (submittedVersion, cc-by)
  • Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, cc-by)
  • Työväentutkimus Vuosikirja (submittedVersion)
  • ORBi (University of Liège) (submittedVersion, other-oa)
  • RWTH Publications (RWTH Aachen) (submittedVersion, cc-by)
  • MPG.PuRe (Max Planck Society) (submittedVersion)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by)
  • Universidad de Chile (submittedVersion, cc-by-nc-nd)
  • CINECA IRIS Institutial Research Information System (University of Genoa) (submittedVersion)
  • Padua Research Archive (University of Padova) (submittedVersion, cc-by)
Funders
  • National Aeronautics and Space Administration
  • Ministerio de Ciencia, Innovación y Universidades
  • Department of Science and Innovation, South Africa
  • European Space Agency
  • European Commission (771776, HORIZON2020)
  • National Research Foundation (150551)
  • Deutsche Forschungsgemeinschaft (EXC 2094390783311, EXC 2094, 390783311)
  • University of Toronto
  • Magyar Tudományos Akadémia
  • Agenzia Spaziale Italiana
  • Max-Planck-Gesellschaft
  • National Astronomical Observatory of Japan
  • Agenția Spațială Română
  • Norsk Romsenter
  • Science and Technology Facilities Council (ST/W006839/1)
  • Fundação para a Ciência e a Tecnologia
  • Agencia Estatal de Investigación (13039, PID2022-141915NB-C22, AEI/10)
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, Euclid Wide Survey, convolutional neural networks, false positive rate

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