Open Access
Issue
A&A
Volume 688, August 2024
Article Number A34
Number of page(s) 28
Section Catalogs and data
DOI https://doi.org/10.1051/0004-6361/202449929
Published online 02 August 2024
  1. Allam, T. J., & McEwen, J. D. 2023, RAS Tech. Instrum., 3, 209 [Google Scholar]
  2. Aslahishahri, M., Ubbens, J., & Stavness, I. 2023, arXiv e-prints [arXiv:2307.08837] [Google Scholar]
  3. Baldry, I. K., Alpaslan, M., Bauer, A. E., et al. 2014, MNRAS, 441, 2440 [Google Scholar]
  4. Barnabè, M., Dutton, A. A., Marshall, P. J., et al. 2012, MNRAS, 423, 1073 [Google Scholar]
  5. Bayliss, M. B., Gladders, M. D., Oguri, M., et al. 2011, ApJ, 727, L26 [NASA ADS] [CrossRef] [Google Scholar]
  6. Benítez, N. 2011, Astrophysics Source Code Library [record ascl:1108.011] [Google Scholar]
  7. Bertin, E., & Arnouts, S. 1996, A&AS, 117, 393 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  8. Bolton, A. S., Burles, S., Koopmans, L. V. E., et al. 2008, ApJ, 682, 964 [Google Scholar]
  9. Boylan-Kolchin, M., Springel, V., White, S. D. M., Jenkins, A., & Lemson, G. 2009, MNRAS, 398, 1150 [Google Scholar]
  10. Brownstein, J. R., Bolton, A. S., Schlegel, D. J., et al. 2011, ApJ, 744, 41 [Google Scholar]
  11. Cañameras, R., Schuldt, S., Suyu, S. H., et al. 2020, A&A, 644, A163 [Google Scholar]
  12. Canameras, R., Schuldt, S., Shu, Y., et al. 2021, A&A, 653, L6 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  13. Canameras, R., Schuldt, S., Shu, Y., et al. 2023, arXiv e-prints [arXiv:2306.03136] [Google Scholar]
  14. Cao, S., Biesiada, M., Gavazzi, R., Piórkowska, A., & Zhu, Z.-H. 2015, ApJ, 806, 185 [NASA ADS] [CrossRef] [Google Scholar]
  15. Cao, S., Li, X., Biesiada, M., et al. 2017, ApJ, 835, 92 [NASA ADS] [CrossRef] [Google Scholar]
  16. Capaccioli, M., & Schipani, P. 2011, The Messenger, 146, 27 [NASA ADS] [Google Scholar]
  17. Carion, N., Massa, F., Synnaeve, G., et al. 2020, in Computer Vision – ECCV 2020, eds. A. Vedaldi, H. Bischof, T. Brox, & J.-M. Frahm (Cham: Springer International Publishing), 213 [Google Scholar]
  18. Chan, J. H. H., Suyu, S. H., More, A., et al. 2016, ApJ, 832, 135 [NASA ADS] [CrossRef] [Google Scholar]
  19. Chan, J. H. H., Suyu, S. H., Sonnenfeld, A., et al. 2020, A&A, 636, A87 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  20. Chen, P., Wu, L., & Wang, L. 2023, Appl. Sci., 13, 10258 [CrossRef] [Google Scholar]
  21. Chou, Y.-L., Moreira, C., Bruza, P., Ouyang, C., & Jorge, J. 2022, Inf. Fusion, 81, 59 [CrossRef] [Google Scholar]
  22. Collett, T. E. 2015, ApJ, 811, 20 [NASA ADS] [CrossRef] [Google Scholar]
  23. Collett, T. E., & Auger, M. W. 2014, MNRAS, 443, 969 [NASA ADS] [CrossRef] [Google Scholar]
  24. Davies, A., Serjeant, S., & Bromley, J. M. 2019, MNRAS, 487, 5263 [NASA ADS] [CrossRef] [Google Scholar]
  25. de Jong, J. T. A., Verdoes Kleijn, G. A., Kuijken, K. H., & Valentijn, E. A. 2013, Exp. Astron., 35, 25 [Google Scholar]
  26. de Jong, J. T. A., Verdoes Kleijn, G. A., Boxhoorn, D. R., et al. 2015, A&A, 582, A62 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  27. de Jong, J. T. A., Verdoes Kleijn, G. A., Erben, T., et al. 2017, A&A, 604, A134 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  28. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. 2019, arXiv e-prints [arXiv:1810.04805] [Google Scholar]
  29. Diehl, H. T., Buckley-Geer, E. J., Lindgren, K. A., et al. 2017, ApJS, 232, 15 [NASA ADS] [CrossRef] [Google Scholar]
  30. Donoso-Oliva, C., Becker, I., Protopapas, P., et al. 2023, A&A, 670, A54 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  31. Dosovitskiy, A., Beyer, L., Kolesnikov, A., et al. 2021, in 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3–7, 2021 (OpenReview.net) [Google Scholar]
  32. Driver, S. P., Norberg, P., Baldry, I. K., et al. 2009, Astron. Geophys., 50, 5.12 [NASA ADS] [CrossRef] [Google Scholar]
  33. Driver, S. P., Hill, D. T., Kelvin, L. S., et al. 2011, MNRAS, 413, 971 [Google Scholar]
  34. Driver, S. P., Bellstedt, S., Robotham, A. S. G., et al. 2022, MNRAS, 513, 439 [NASA ADS] [CrossRef] [Google Scholar]
  35. Dye, S., & Warren, S. J. 2005, ApJ, 623, 31 [NASA ADS] [CrossRef] [Google Scholar]
  36. Eisenstein, D. J., Annis, J., Gunn, J. E., et al. 2001, AJ, 122, 2267 [Google Scholar]
  37. Faure, C., Kneib, J.-P., Covone, G., et al. 2008, ApJS, 176, 19 [NASA ADS] [CrossRef] [Google Scholar]
  38. Ferrara, E. 2024, Science, 6 [Google Scholar]
  39. Garvin, E. O., Kruk, S., Cornen, C., et al. 2022, A&A, 667, A141 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  40. Geach, J. E., More, A., Verma, A., et al. 2015, MNRAS, 452, 502 [NASA ADS] [CrossRef] [Google Scholar]
  41. Gentile, F., Tortora, C., Covone, G., et al. 2021, MNRAS, 510, 500 [NASA ADS] [CrossRef] [Google Scholar]
  42. He, K., Zhang, X., Ren, S., & Sun, J. 2016, in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770 [Google Scholar]
  43. He, Z., Er, X., Long, Q., et al. 2020, MNRAS, 497, 556 [NASA ADS] [CrossRef] [Google Scholar]
  44. Hennawi, J. F., Gladders, M. D., Oguri, M., et al. 2008, AJ, 135, 664 [NASA ADS] [CrossRef] [Google Scholar]
  45. Hezaveh, Y., Dalal, N., Holder, G., et al. 2016, J. Cosmol. Astropart. Phys., 2016, 048 [CrossRef] [Google Scholar]
  46. Holloway, P., Marshall, P. J., Verma, A., et al. 2024, MNRAS, 530, 1297 [NASA ADS] [CrossRef] [Google Scholar]
  47. Holwerda, B. W., Baldry, I. K., Alpaslan, M., et al. 2015, MNRAS, 449, 4277 [NASA ADS] [CrossRef] [Google Scholar]
  48. Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. 2017, 2261 [Google Scholar]
  49. Huang, X., Storfer, C., Ravi, V., et al. 2020, ApJ, 894, 78 [NASA ADS] [CrossRef] [Google Scholar]
  50. Huang, X., Storfer, C., Gu, A., et al. 2021, ApJ, 909, 27 [NASA ADS] [CrossRef] [Google Scholar]
  51. Huang, K.-W., Chih-Fan Chen, G., Chang, P.-W., et al. 2022, arXiv e-prints [arXiv:2210.04143] [Google Scholar]
  52. Hwang, S. Y., Sabiu, C. G., Park, I., & Hong, S. E. 2023, J. Cosmol. Astropart. Phys., 2023, 075 [CrossRef] [Google Scholar]
  53. Jacobs, C., Collett, T., Glazebrook, K., et al. 2019, ApJS, 243, 17 [Google Scholar]
  54. Jaelani, A. T., More, A., Oguri, M., et al. 2020, MNRAS, 495, 1291 [Google Scholar]
  55. Jaelani, A. T., Rusu, C. E., Kayo, I., et al. 2021, MNRAS, 502, 1487 [NASA ADS] [CrossRef] [Google Scholar]
  56. Jia, P., Sun, R., Li, N., et al. 2023, AJ, 165, 26 [NASA ADS] [CrossRef] [Google Scholar]
  57. Khan, S., Naseer, M., Hayat, M., et al. 2022, ACM Comput. Surv., 54, 1 [CrossRef] [Google Scholar]
  58. Knabel, S., Steele, R. L., Holwerda, B. W., et al. 2020, AJ, 160, 223 [NASA ADS] [CrossRef] [Google Scholar]
  59. Knabel, S., Holwerda, B. W., Nightingale, J., et al. 2023, MNRAS, 520, 804 [NASA ADS] [CrossRef] [Google Scholar]
  60. Krizhevsky, A., Sutskever, I., & Hinton, G. E. 2012, in Advances in Neural Information Processing Systems, 25, eds. F. Pereira, C. Burges, L. Bottou, & K. Weinberger (Curran Associates, Inc.) [Google Scholar]
  61. Kuijken, K. 2008, A&A, 482, 1053 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  62. Kuijken, K. 2011, The Messenger, 146, 8 [NASA ADS] [Google Scholar]
  63. Kuijken, K., Heymans, C., Dvornik, A., et al. 2019, A&A, 625, A2 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  64. La Barbera, F., de Carvalho, R. R., Kohl-Moreira, J. L., et al. 2008, PASP, 120, 681 [NASA ADS] [CrossRef] [Google Scholar]
  65. Laureijs, R., Amiaux, J., Arduini, S., et al. 2011, arXiv e-prints [arXiv:1110.3193] [Google Scholar]
  66. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. 1998, Proc. IEEE, 86, 2278 [Google Scholar]
  67. Lewis, D. D., & Gale, W. A. 1994, 3 [Google Scholar]
  68. Li, R., Napolitano, N. R., Tortora, C., et al. 2020, ApJ, 899, 30 [Google Scholar]
  69. Li, R., Napolitano, N. R., Spiniello, C., et al. 2021, ApJ, 923, 16 [NASA ADS] [CrossRef] [Google Scholar]
  70. Li, X., Ding, H., Yuan, H., et al. 2023, arXiv e-prints [arXiv:2304.09854] [Google Scholar]
  71. Liske, J., Baldry, I. K., Driver, S. P., et al. 2015, MNRAS, 452, 2087 [Google Scholar]
  72. LSST Science Collaboration (Abell, P. A., et al.) 2009, arXiv e-prints [arXiv:0912.0201] [Google Scholar]
  73. Lupton, R., Blanton, M. R., Fekete, G., et al. 2004, PASP, 116, 133 [NASA ADS] [CrossRef] [Google Scholar]
  74. Marshall, P. J., Verma, A., More, A., et al. 2016, MNRAS, 455, 1171 [NASA ADS] [CrossRef] [Google Scholar]
  75. Merz, G., Liu, Y., Burke, C. J., et al. 2023, MNRAS, 526, 1122 [NASA ADS] [CrossRef] [Google Scholar]
  76. Metcalf, R. B., & Petkova, M. 2014, MNRAS, 445, 1942 [NASA ADS] [CrossRef] [Google Scholar]
  77. Metcalf, R. B., Meneghetti, M., Avestruz, C., et al. 2019, A&A, 625, A119 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  78. Miyazaki, S., Komiyama, Y., Nakaya, H., et al. 2012, SPIE Conf. Ser., 8446, 84460Z [NASA ADS] [Google Scholar]
  79. More, A., Cabanac, R., More, S., et al. 2012, ApJ, 749, 38 [NASA ADS] [CrossRef] [Google Scholar]
  80. More, A., Verma, A., Marshall, P. J., et al. 2015, MNRAS, 455, 1191 [Google Scholar]
  81. More, A., Lee, C.-H., Oguri, M., et al. 2017, MNRAS, 465, 2411 [NASA ADS] [CrossRef] [Google Scholar]
  82. Mumuni, A., & Mumuni, F. 2022, Array, 16, 100258 [CrossRef] [Google Scholar]
  83. Negrello, M., Hopwood, R., Zotti, G. D., et al. 2010, Science, 330, 800 [NASA ADS] [CrossRef] [Google Scholar]
  84. Negrello, M., Amber, S., Amvrosiadis, A., et al. 2016, MNRAS, 465, 3558 [Google Scholar]
  85. Neri, R., Cox, P., Omont, A., et al. 2020, A&A, 635, A7 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  86. Nightingale, J. W., Massey, R. J., Harvey, D. R., et al. 2019, MNRAS, 489, 2049 [NASA ADS] [Google Scholar]
  87. Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. 2019, Science, 366, 447 [CrossRef] [PubMed] [Google Scholar]
  88. O’Donnell, J. H., Wilkinson, R. D., Diehl, H. T., et al. 2022, ApJS, 259, 27 [CrossRef] [Google Scholar]
  89. Oguri, M., & Marshall, P. J. 2010, MNRAS, 405, 2579 [NASA ADS] [Google Scholar]
  90. Pan, S. J., & Yang, Q. 2010, IEEE Trans. Knowl. Data Eng., 22, 1345 [Google Scholar]
  91. Paul, S., & Chen, P.-Y. 2022, Proc. AAAI Conf. Artif. Intell., 36, 2071 [Google Scholar]
  92. Petkova, M., Metcalf, R. B., & Giocoli, C. 2014, MNRAS, 445, 1954 [NASA ADS] [CrossRef] [Google Scholar]
  93. Petrillo, C. E., Tortora, C., Chatterjee, S., et al. 2017, MNRAS, 472, 1129 [Google Scholar]
  94. Petrillo, C. E., Tortora, C., Chatterjee, S., et al. 2018, MNRAS, 482, 807 [NASA ADS] [Google Scholar]
  95. Petrillo, C. E., Tortora, C., Vernardos, G., et al. 2019, MNRAS, 484, 3879 [Google Scholar]
  96. Rezaei, S., McKean, J. P., Biehl, M., de Roo, W., & Lafontaine, A. 2022, MNRAS, 517, 1156 [NASA ADS] [CrossRef] [Google Scholar]
  97. Ribeiro, M. T., Singh, S., & Guestrin, C. 2016, arXiv e-prints [arXiv:1602.04938] [Google Scholar]
  98. Rigby, J. R., Bayliss, M. B., Gladders, M. D., et al. 2014, ApJ, 790, 44 [NASA ADS] [CrossRef] [Google Scholar]
  99. Rivera, J., Baker, A. J., Gallardo, P. A., et al. 2019, ApJ, 879, 95 [NASA ADS] [CrossRef] [Google Scholar]
  100. Rojas, K., Savary, E., Clément, B., et al. 2022, A&A, 668, A73 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  101. Rojas, K., Collett, T. E., Ballard, D., et al. 2023, MNRAS, 523, 4413 [NASA ADS] [CrossRef] [Google Scholar]
  102. Schaefer, C., Geiger, M., Kuntzer, T., & Kneib, J.-P. 2018, A&A, 611, A2 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  103. Schneider, P., Ehlers, J., & Falco, E. E. 1992 [Google Scholar]
  104. Seidel, G., & Bartelmann, M. 2007, A&A, 472, 341 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  105. Selvaraju, R. R., Cogswell, M., Das, A., et al. 2019, Int. J. Comput. Vis., 128, 336 [Google Scholar]
  106. Shu, Y., Brownstein, J. R., Bolton, A. S., et al. 2017, ApJ, 851, 48 [Google Scholar]
  107. Shu, Y., Cañameras, R., Schuldt, S., et al. 2022, A&A, 662, A4 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  108. Simonyan, K., & Zisserman, A. 2015, arXiv e-prints [arXiv:1409.1556] [Google Scholar]
  109. Sonnenfeld, A., Chan, J. H. H., Shu, Y., et al. 2017, PASJ, 70 [Google Scholar]
  110. Sonnenfeld, A., Verma, A., More, A., et al. 2020, A&A, 642, A148 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  111. Spilker, J. S., Marrone, D. P., Aravena, M., et al. 2016, ApJ, 826, 112 [NASA ADS] [CrossRef] [Google Scholar]
  112. Stein, G., Blaum, J., Harrington, P., Medan, T., & Lukić, Z. 2022, ApJ, 932, 107 [NASA ADS] [CrossRef] [Google Scholar]
  113. Storfer, C., Huang, X., Gu, A., et al. 2022, arXiv e-prints [arXiv:2206.02764] [Google Scholar]
  114. Szegedy, C., Liu, W., Jia, Y., et al. 2015, in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1 [Google Scholar]
  115. Tan, M., & Le, Q. 2019, International Conference on Machine Learning, 6105 [Google Scholar]
  116. Thuruthipilly, H., Zadrozny, A., Pollo, A., & Biesiada, M. 2022, A&A, 664, A4 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  117. Thuruthipilly, H., Grespan, M., & Zadrożny, A. 2024a, AIP Conf. Proc., 3061, 040003 [NASA ADS] [CrossRef] [Google Scholar]
  118. Thuruthipilly, H., Junais, Pollo, A., et al. 2024b, A&A, 682, A4 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  119. Timmis, I., & Shamir, L. 2017, ApJS, 231, 2 [NASA ADS] [CrossRef] [Google Scholar]
  120. Tran, K.-V. H., Harshan, A., Glazebrook, K., et al. 2022, AJ, 164, 148 [NASA ADS] [CrossRef] [Google Scholar]
  121. Turner, E. L., Ostriker, J. P., & Gott, J. R. I., 1984, ApJ, 284, 1 [NASA ADS] [CrossRef] [Google Scholar]
  122. Vaswani, A., Shazeer, N., Parmar, N., et al. 2017, in Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4–9, 2017, Long Beach, CA, USA, 5998 [Google Scholar]
  123. Verma, A., Collett, T., Smith, G. P., Strong Lensing Science Collaboration, & the DESC Strong Lensing Science Working Group. 2019 arXiv e-prints [arXiv:1902.05141] [Google Scholar]
  124. Wang, Z., Ng, P., Ma, X., Nallapati, R., & Xiang, B. 2019, Assoc. Computat. Linguist., 5878 [Google Scholar]
  125. Wei, J.-J., Chen, Y., Cao, S., & Wu, X.-F. 2022, ApJ, 927, L1 [NASA ADS] [CrossRef] [Google Scholar]
  126. Wenger, M., Ochsenbein, F., Egret, D., et al. 2000, A&ASS, 143, 9 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  127. Wilde, J., Serjeant, S., Bromley, J. M., et al. 2022, MNRAS, 512, 3464 [Google Scholar]
  128. Wong, K. C., Chan, J. H. H., Chao, D. C.-Y., et al. 2022, PASJ, 74, 1209 [CrossRef] [Google Scholar]
  129. Wortsman, M., Ilharco, G., Gadre, S. Y., et al. 2022, PMLR, 162, 23965 [Google Scholar]
  130. Xu, M., Yoon, S., Fuentes, A., & Park, D. S. 2023, Pattern Recognit., 137, 109347 [NASA ADS] [CrossRef] [Google Scholar]
  131. Yip, K. H., Changeat, Q., Nikolaou, N., et al. 2021, AJ, 162, 195 [NASA ADS] [CrossRef] [Google Scholar]
  132. Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. 2014, in Advances in Neural Information Processing Systems, 27, eds. Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, & K. Weinberger (Curran Associates, Inc.) [Google Scholar]
  133. Yu, J., Wang, Z., Vasudevan, V., et al. 2022, Trans. Mach. Learn. Res., 2022 [Google Scholar]
  134. Zaborowski, E. A., Drlica-Wagner, A., Ashmead, F., et al. 2023, ApJ, 954, 68 [NASA ADS] [CrossRef] [Google Scholar]

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