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Home All issues Volume 692 (December 2024) A&A, 692 (2024) A72 References
Open Access
Issue
A&A
Volume 692, December 2024
Article Number A72
Number of page(s) 22
Section Numerical methods and codes
DOI https://doi.org/10.1051/0004-6361/202347072
Published online 03 December 2024
  1. Abolfathi, B., Aguado, D. S., Aguilar, G., et al. 2018, ApJS, 235, 42 [NASA ADS] [CrossRef] [Google Scholar]
  2. Aihara, H., Arimoto, N., Armstrong, R., et al. 2018a, PASJ, 70, S4 [NASA ADS] [Google Scholar]
  3. Aihara, H., Armstrong, R., Bickerton, S., et al. 2018b, PASJ, 70, S8 [NASA ADS] [Google Scholar]
  4. Aihara, H., AlSayyad, Y., Ando, M., et al. 2019, PASJ, 106, 12 [Google Scholar]
  5. Aniyan, A. K., & Thorat, K. 2017, ApJS, 230, 20 [Google Scholar]
  6. Astropy Collaboration (Price-Whelan, A.M., et al.) 2018, AJ, 156, 123 [NASA ADS] [CrossRef] [Google Scholar]
  7. Bautista, J. E., Vargas-Magaña, M., Dawson, K. S., et al. 2018, ApJ, 863, 110 [NASA ADS] [CrossRef] [Google Scholar]
  8. Beckwith, S. V. W., Stiavelli, M., Koekemoer, A. M., et al. 2006, AJ, 132, 1729 [Google Scholar]
  9. Bertin, E., & Arnouts, S. 1996, A&AS, 117, 393 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  10. Bolton, A. S., Burles, S., Koopmans, L. V. E., et al. 2008, ApJ, 682, 964 [Google Scholar]
  11. Bonvin, V., Courbin, F., Suyu, S. H., et al. 2017, MNRAS, 465, 4914 [NASA ADS] [CrossRef] [Google Scholar]
  12. Bosch, J., Armstrong, R., Bickerton, S., et al. 2018, PASJ, 70, S5 [Google Scholar]
  13. Brownstein, J. R., Bolton, A. S., Schlegel, D. J., et al. 2012, ApJ, 744, 41 [NASA ADS] [CrossRef] [Google Scholar]
  14. Cañameras, R., Schuldt, S., Suyu, S. H., et al. 2020, A&A, 644, A163 [Google Scholar]
  15. Cañameras, R., Schuldt, S., Shu, Y., et al. 2021, A&A, 653, L6 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  16. Chan, J. H. H., Suyu, S. H., Sonnenfeld, A., et al. 2020, A&A, 636, A87 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  17. Cheng, T.-Y., Li, N., Conselice, C. J., et al. 2020, MNRAS, 494, 3750 [NASA ADS] [CrossRef] [Google Scholar]
  18. Cheng, T.-Y., Huertas-Company, M., Conselice, C. J., et al. 2021, MNRAS, 503, 4446 [NASA ADS] [CrossRef] [Google Scholar]
  19. Cohen, T. S., & Welling, M. 2016, arXiv e-prints [arXiv:1602.07576] [Google Scholar]
  20. Collett, T. E. 2015, ApJ, 811, 20 [NASA ADS] [CrossRef] [Google Scholar]
  21. Craig, P., O’Connor, K., Chakrabarti, S., et al. 2024, MNRAS, 534, 1077 [NASA ADS] [CrossRef] [Google Scholar]
  22. Diehl, H. T., Buckley-Geer, E. J., Lindgren, K. A., et al. 2017, ApJS, 232, 15 [NASA ADS] [CrossRef] [Google Scholar]
  23. Dieleman, S., Willett, K. W., & Dambre, J. 2015, MNRAS, 450, 1441 [NASA ADS] [CrossRef] [Google Scholar]
  24. D’Isanto, A., & Polsterer, K.L. 2018, A&A, 609, A111 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  25. Faure, C., Kneib, J.-P., Covone, G., et al. 2008, ApJS, 176, 19 [NASA ADS] [CrossRef] [Google Scholar]
  26. Gavazzi, R., Marshall, P. J., Treu, T., & Sonnenfeld, A. 2014, ApJ, 785, 144 [Google Scholar]
  27. Goldstein, D. A., & Nugent, P. E. 2017, ApJ, 834, L5 [Google Scholar]
  28. Goldstein, D. A., Nugent, P. E., & Goobar, A. 2019, ApJS, 243, 6 [NASA ADS] [CrossRef] [Google Scholar]
  29. Gong, Y., Liu, X., Cao, Y., et al. 2019, ApJ, 883, 203 [NASA ADS] [CrossRef] [Google Scholar]
  30. Green, J., Schechter, P., Baltay, C., et al. 2012, arXiv e-prints [arXiv:1208.4012] [Google Scholar]
  31. Hansen, L., & Salamon, P. 1990, IEEE Trans. Pattern Anal. Mach. Intell., 12, 993 [CrossRef] [Google Scholar]
  32. Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357 [NASA ADS] [CrossRef] [Google Scholar]
  33. He, K., Zhang, X., Ren, S., & Sun, J. 2016, arXiv e-prints [arXiv:1603.05027] [Google Scholar]
  34. He, Z., Er, X., Long, Q., et al. 2020, MNRAS, 497, 556 [NASA ADS] [CrossRef] [Google Scholar]
  35. Hezaveh, Y. D., Perreault Levasseur, L., & Marshall, P. J. 2017, Nature, 548, 555 [Google Scholar]
  36. Hocking, A., Geach, J. E., Sun, Y., & Davey, N. 2018, MNRAS, 473, 1108 [CrossRef] [Google Scholar]
  37. Huang, X., Storfer, C., Gu, A., et al. 2021, ApJ, 909, 27 [NASA ADS] [CrossRef] [Google Scholar]
  38. Hunter, J. D. 2007, Comput. Sci. Eng., 9, 90 [NASA ADS] [CrossRef] [Google Scholar]
  39. Inami, H., Bacon, R., Brinchmann, J., et al. 2017, A&A, 608, A2 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  40. Ioffe, S., & Szegedy, C. 2015, Proc. Int. Conf. Mach. Learn., 37, 448 [Google Scholar]
  41. Ivezić, Ž., Kahn, S. M., Tyson, J. A., et al. 2019, ApJ, 873, 111 [Google Scholar]
  42. Jacobs, C., Glazebrook, K., Collett, T., More, A., & McCarthy, C. 2017, MNRAS, 471, 167 [Google Scholar]
  43. Jacobs, C., Collett, T., Glazebrook, K., et al. 2019a, ApJS, 243, 17 [Google Scholar]
  44. Jacobs, C., Collett, T., Glazebrook, K., et al. 2019b, MNRAS, 484, 5330 [NASA ADS] [CrossRef] [Google Scholar]
  45. Jacobs, C., Glazebrook, K., Qin, A., & Collett, T. 2022, Astron. Comput., 38, 100535 [NASA ADS] [CrossRef] [Google Scholar]
  46. Jaelani, A. T., More, A., Oguri, M., et al. 2020, MNRAS, 495, 1291 [Google Scholar]
  47. Krizhevsky, A., Sutskever, I., & Hinton, G. E. 2012, in Advances in Neural Information Processing Systems, eds. F. Pereira, C. Burges, L. Bottou, & K. Weinberger (New York: Curran Associates, Inc.), 25 [Google Scholar]
  48. Krogh, A., & Vedelsby, J. 1994, in Proceedings of the 7th International Conference on Neural Information Processing Systems, NIPS’94 (Cambridge, MA, USA: MIT Press), 231 [Google Scholar]
  49. Lanusse, F., Ma, Q., Li, N., et al. 2018, MNRAS, 473, 3895 [Google Scholar]
  50. Laureijs, R., Amiaux, J., Arduini, S., et al. 2011, arXiv e-prints [arXiv:1110.3193] [Google Scholar]
  51. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. 1998, in Proceedings of the IEEE, 2278 [Google Scholar]
  52. Lemson, G., & Virgo Consortium, T. 2006, arXiv e-prints [arXiv:astro-ph/0608019] [Google Scholar]
  53. Li, R., Napolitano, N. R., Spiniello, C., et al. 2021, ApJ, 923, 16 [NASA ADS] [CrossRef] [Google Scholar]
  54. Li, R., Napolitano, N. R., Roy, N., et al. 2022, ApJ, 929, 152 [NASA ADS] [CrossRef] [Google Scholar]
  55. Lupton, R., Gunn, J. E., Ivezic, Z., Knapp, G. R., & Kent, S. 2001, ASP Conf. Ser., 238, 269 [NASA ADS] [Google Scholar]
  56. Margalef-Bentabol, B., Huertas-Company, M., Charnock, T., et al. 2020, MNRAS, 496, 2346 [Google Scholar]
  57. Marshall, P. J., Verma, A., More, A., et al. 2016, MNRAS, 455, 1171 [NASA ADS] [CrossRef] [Google Scholar]
  58. Martin, G., Kaviraj, S., Hocking, A., Read, S. C., & Geach, J. E. 2020, MNRAS, 491, 1408 [Google Scholar]
  59. McKean, J., Jackson, N., Vegetti, S., et al. 2015, in Advancing Astrophysics with the Square Kilometre Array (AASKA14), 84 [Google Scholar]
  60. Metcalf, R. B., Meneghetti, M., Avestruz, C., et al. 2019, A&A, 625, A119 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  61. More, A., Verma, A., Marshall, P. J., et al. 2016, MNRAS, 455, 1191 [NASA ADS] [CrossRef] [Google Scholar]
  62. More, A., Cañameras, R., Jaelani, A. T., et al. 2024, MNRAS, 533, 525 [CrossRef] [Google Scholar]
  63. Nagam, B. C., Koopmans, L. V. E., Valentijn, E. A., et al. 2023, MNRAS, 523, 4188 [NASA ADS] [CrossRef] [Google Scholar]
  64. Nair, V. & Hinton, G. E. 2010, in, Proceedings of the 27th International Conference on Machine Learning, ICML, eds. J. Fürnkranz, & T. Joachims (USA: Omnipress), 807 [Google Scholar]
  65. Paszke, A., Gross, S., Massa, F., et al. 2019, arXiv e-prints, [arXiv:1912.01703] [Google Scholar]
  66. Pearson, J., Maresca, J., Li, N., & Dye, S. 2021, MNRAS, 505, 4362 [CrossRef] [Google Scholar]
  67. Pearson, W. J., Suelves, L. E., Ho, S. C. C., et al. 2022, A&A, 661, A52 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  68. Petrillo, C. E., Tortora, C., Chatterjee, S., et al. 2017, MNRAS, 472, 1129 [Google Scholar]
  69. Planck Collaboration XIII. 2016, A&A, 594, A13 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  70. Pourrahmani, M., Nayyeri, H., & Cooray, A. 2018, ApJ, 856, 68 [NASA ADS] [CrossRef] [Google Scholar]
  71. Ranzato, M. A., Boureau, Y.-L., & LeCun, Y. 2007, in Proceedings of the 20th International Conference on Neural Information Processing Systems, NIPS’07 (Red Hook, NY, USA: Curran Associates Inc.), 1185 [Google Scholar]
  72. Refsdal, S. 1964, MNRAS, 128, 307 [NASA ADS] [CrossRef] [Google Scholar]
  73. Rojas, K., Savary, E., Clément, B., et al. 2022, A&A, 668, A73 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  74. Rojas, K., Collett, T. E., Ballard, D., et al. 2023, MNRAS, 523, 4413 [NASA ADS] [CrossRef] [Google Scholar]
  75. Scaife, A. M. M., & Porter, F. 2021, MNRAS, 503, 2369 [CrossRef] [Google Scholar]
  76. Schaefer, C., Geiger, M., Kuntzer, T., & Kneib, J. P. 2018, A&A, 611, A2 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  77. Schuldt, S., Suyu, S. H., Cañameras, R., et al. 2021a, A&A, 651, A55 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  78. Schuldt, S., Suyu, S. H., Meinhardt, T., et al. 2021b, A&A, 646, A126 [EDP Sciences] [Google Scholar]
  79. Schuldt, S., Cañameras, R., Shu, Y., et al. 2023a, A&A, 671, A147 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  80. Schuldt, S., Suyu, S. H., Cañameras, R., et al. 2023b, A&A, 673, A33 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  81. Shajib, A. J., Vernardos, G., Collett, T. E., et al. 2022, ArXiv e-prints [arXiv:2210.10790] [Google Scholar]
  82. Shajib, A. J., Mozumdar, P., Chen, G. C. F., et al. 2023, A&A, 673, A9 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  83. Shu, Y., Bolton, A. S., Kochanek, C. S., et al. 2016, ApJ, 824, 86 [NASA ADS] [CrossRef] [Google Scholar]
  84. Shu, Y., Bolton, A. S., Mao, S., et al. 2018, ApJ, 864, 91 [NASA ADS] [CrossRef] [Google Scholar]
  85. Shu, Y., Cañameras, R., Schuldt, S., et al. 2022, A&A, 662, A4 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  86. Slijepcevic, I. V., Scaife, A. M. M., Walmsley, M., et al. 2022, MNRAS, 514, 2599 [NASA ADS] [CrossRef] [Google Scholar]
  87. Sonnenfeld, A., Chan, J. H. H., Shu, Y., et al. 2018, PASJ, 70, S29 [Google Scholar]
  88. Sonnenfeld, A., Verma, A., More, A., et al. 2020, A&A, 642, A148 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  89. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. 2014, J. Mach. Learn. Res., 15, 1929 [Google Scholar]
  90. Stein, G., Blaum, J., Harrington, P., Medan, T., & Lukic, Z. 2022, ApJ, 932, 107 [NASA ADS] [CrossRef] [Google Scholar]
  91. Storey-Fisher, K., Huertas-Company, M., Ramachandra, N., et al. 2021, MNRAS, 508, 2946 [NASA ADS] [CrossRef] [Google Scholar]
  92. Suyu, S. H., & Halkola, A. 2010, A&A, 524, A94 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  93. Suyu, S. H., Hensel, S. W., McKean, J. P., et al. 2012, ApJ, 750, 10 [Google Scholar]
  94. Suyu, S. H., Huber, S., Cañameras, R., et al. 2020, A&A, 644, A162 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  95. Suyu, S. H., Goobar, A., Collett, T., More, A., & Vernardos, G. 2024, Space Sci. Rev., 220, 13 [NASA ADS] [CrossRef] [Google Scholar]
  96. Tadaki, K.-I., Iye, M., Fukumoto, H., et al. 2020, MNRAS, 496, 4276 [NASA ADS] [CrossRef] [Google Scholar]
  97. Tanaka, M. 2015, ApJ, 801, 20 [Google Scholar]
  98. Thuruthipilly, H., Zadrozny, A., Pollo, A., & Biesiada, M. 2022, A&A, 664, A4 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  99. Tohill, C., Ferreira, L., Conselice, C. J., Bamford, S. P., & Ferrari, F. 2021, ApJ, 916, 4 [NASA ADS] [CrossRef] [Google Scholar]
  100. Tran, K.-V. H., Harshan, A., Glazebrook, K., et al. 2022, AJ, 164, 148 [NASA ADS] [CrossRef] [Google Scholar]
  101. Tuccillo, D., Huertas-Company, M., Decencière, E., et al. 2018, MNRAS, 475, 894 [NASA ADS] [CrossRef] [Google Scholar]
  102. Turner, E. L., Ostriker, J. P., & Gott, J. R. I. 1984, ApJ, 284, 1 [NASA ADS] [CrossRef] [Google Scholar]
  103. Vaswani, A., Shazeer, N., Parmar, N., et al. 2017, Advances in Neural Information Processing Systems, eds. I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (USA: Curran Associates, Inc.), 30 [Google Scholar]
  104. Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nat. Methods, 17, 261 [Google Scholar]
  105. Walmsley, M., Lintott, C., Géron, T., et al. 2022, MNRAS, 509, 3966 [Google Scholar]
  106. Wen, Z. L., Han, J. L., & Liu, F. S. 2012, ApJS, 199, 34 [Google Scholar]
  107. Wilde, J., Serjeant, S., Bromley, J. M., et al. 2022, MNRAS, 512, 3464 [Google Scholar]
  108. Willett, K. W., Lintott, C. J., Bamford, S. P., et al. 2013, MNRAS, 435, 2835 [Google Scholar]
  109. Willett, K. W., Galloway, M. A., Bamford, S. P., et al. 2017, MNRAS, 464, 4176 [NASA ADS] [CrossRef] [Google Scholar]
  110. Wong, K. C., Sonnenfeld, A., Chan, J. H. H., et al. 2018, ApJ, 867, 107 [Google Scholar]
  111. Wong, K. C., Suyu, S. H., Chen, G. C. F., et al. 2020, MNRAS, 498, 1420 [Google Scholar]

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