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Home All issues Volume 682 (February 2024) A&A, 682 (2024) A4 References
Open Access
Issue
A&A
Volume 682, February 2024
Article Number A4
Number of page(s) 23
Section Catalogs and data
DOI https://doi.org/10.1051/0004-6361/202347649
Published online 26 January 2024
  1. Abbott, T. M. C., Abdalla, F. B., Allam, S., et al. 2018, ApJS, 239, 18 [Google Scholar]
  2. Abbott, T. M. C., Adamów, M., Aguena, M., et al. 2021, ApJS, 255, 20 [NASA ADS] [CrossRef] [Google Scholar]
  3. Aihara, H., AlSayyad, Y., Ando, M., et al. 2022, PASJ, 74, 247 [NASA ADS] [CrossRef] [Google Scholar]
  4. Alabi, A. B., Romanowsky, A. J., Forbes, D. A., Brodie, J. P., & Okabe, N. 2020, MNRAS, 496, 3182 [NASA ADS] [CrossRef] [Google Scholar]
  5. Allam, Tarek, J., & McEwen, J. D. 2021, arXiv e-prints [arXiv:2105.06178] [Google Scholar]
  6. Amorisco, N. C., & Loeb, A. 2016, MNRAS, 459, L51 [NASA ADS] [CrossRef] [Google Scholar]
  7. Bamford, S. P., Nichol, R. C., Baldry, I. K., et al. 2009, MNRAS, 393, 1324 [NASA ADS] [CrossRef] [Google Scholar]
  8. Benavides, J. A., Sales, L. V., Abadi, M. G., et al. 2023, MNRAS, 522, 1033 [NASA ADS] [CrossRef] [Google Scholar]
  9. Bernstein, G. M., Nichol, R. C., Tyson, J. A., Ulmer, M. P., & Wittman, D. 1995, AJ, 110, 1507 [Google Scholar]
  10. Bertin, E., & Arnouts, S. 1996, A&AS, 117, 393 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  11. Bilicki, M., Jarrett, T. H., Peacock, J. A., Cluver, M. E., & Steward, L. 2014, ApJS, 210, 9 [Google Scholar]
  12. Bothun, G. D., Impey, C. D., Malin, D. F., & Mould, J. R. 1987, AJ, 94, 23 [NASA ADS] [CrossRef] [Google Scholar]
  13. Bothun, G. D., Schombert, J. M., Impey, C. D., Sprayberry, D., & McGaugh, S. S. 1993, AJ, 106, 530 [NASA ADS] [CrossRef] [Google Scholar]
  14. Bothun, G., Impey, C., & McGaugh, S. 1997, PASP, 109, 745 [NASA ADS] [CrossRef] [Google Scholar]
  15. Burkholder, V., Impey, C., & Sprayberry, D. 2001, AJ, 122, 2318 [NASA ADS] [CrossRef] [Google Scholar]
  16. Buyle, P., De Rijcke, S., Michielsen, D., Baes, M., & Dejonghe, H. 2005, MNRAS, 360, 853 [NASA ADS] [CrossRef] [Google Scholar]
  17. Cabrera-Vives, G., Reyes, I., Förster, F., Estévez, P. A., & Maureira, J.-C. 2017, ApJ, 836, 97 [NASA ADS] [CrossRef] [Google Scholar]
  18. 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]
  19. Carleton, T., Cohen, S. H., Frye, B. L., et al. 2023, ApJ, 953, 83 [NASA ADS] [CrossRef] [Google Scholar]
  20. Chamba, N., Trujillo, I., & Knapen, J. H. 2022, A&A, 667, A87 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  21. Chen, P.-C., Tsai, H., Bhojanapalli, S., et al. 2021, in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Online and Punta Cana, Dominican Republic: Association for Computational Linguistics), 2974 [CrossRef] [Google Scholar]
  22. Chen, Z., Duan, Y., Wang, W., et al. 2023, in The Eleventh International Conference on Learning Representations [Google Scholar]
  23. Clevert, D., Unterthiner, T., & Hochreiter, S. 2016, in 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings, eds. Y. Bengio, & Y. LeCun [Google Scholar]
  24. Cohen, Y., van Dokkum, P., Danieli, S., et al. 2018, ApJ, 868, 96 [Google Scholar]
  25. Conselice, C. J. 2018, Res. Notes Am. Astron. Soc., 2, 43 [Google Scholar]
  26. Conselice, C. J., Gallagher, John S. I., & Wyse, R. F. G. 2003a, AJ, 125, 66 [CrossRef] [Google Scholar]
  27. Conselice, C. J., O’Neil, K., Gallagher, J. S., & Wyse, R. F. G. 2003b, ApJ, 591, 167 [NASA ADS] [CrossRef] [Google Scholar]
  28. Dalcanton, J. J., Spergel, D. N., Gunn, J. E., Schmidt, M., & Schneider, D. P. 1997, AJ, 114, 635 [Google Scholar]
  29. Danieli, S., van Dokkum, P., Merritt, A., et al. 2017, ApJ, 837, 136 [NASA ADS] [CrossRef] [Google Scholar]
  30. Davies, A., Serjeant, S., & Bromley, J. M. 2019, MNRAS, 487, 5263 [NASA ADS] [CrossRef] [Google Scholar]
  31. De Vicente, J., Sánchez, E., & Sevilla-Noarbe, I. 2016, MNRAS, 459, 3078 [NASA ADS] [CrossRef] [Google Scholar]
  32. Dey, A., Schlegel, D. J., Lang, D., et al. 2019, AJ, 157, 168 [Google Scholar]
  33. Di Cintio, A., Brook, C. B., Dutton, A. A., et al. 2017, MNRAS, 466, L1 [NASA ADS] [CrossRef] [Google Scholar]
  34. Dietterich, T. G. 2000, Multiple Classifier Systems (Berlin: Springer) 1 [Google Scholar]
  35. Domingos, P., & Hulten, G. 1999, in Proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining, ACM, 155 [CrossRef] [Google Scholar]
  36. 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]
  37. Driver, S. P. 1999, ApJ, 526, L69 [NASA ADS] [CrossRef] [Google Scholar]
  38. Du, W., Cheng, C., Zheng, Z., & Wu, H. 2020, AJ, 159, 138 [CrossRef] [Google Scholar]
  39. Efron, B., & Gong, G. 1983, Am. Stat., 37, 36 [Google Scholar]
  40. Erwin, P. 2015, ApJ, 799, 226 [Google Scholar]
  41. Ettori, S., & Balestra, I. 2009, A&A, 496, 343 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  42. Euclid Collaboration (Scaramella, R., et al.) 2022, A&A, 662, A112 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  43. Fitzpatrick, E. L. 1999, PASP, 111, 63 [Google Scholar]
  44. Flaugher, B., Diehl, H. T., Honscheid, K., et al. 2015, AJ, 150, 150 [Google Scholar]
  45. Fu, J., Liu, J., Tian, H., et al. 2019, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (Los Alamitos, CA, USA: IEEE Computer Society), 3141 [CrossRef] [Google Scholar]
  46. Galaz, G., Frayer, D. T., Blaña, M., et al. 2022, ApJ, 940, L37 [NASA ADS] [CrossRef] [Google Scholar]
  47. Glorot, X., & Bengio, Y. 2010, in JMLR Proceedings, 9, Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2010, Chia Laguna Resort, Sardinia, Italy, May 13–15, 2010, eds. Y. W. Teh & D. M. Titterington (JMLR.org), 249 [Google Scholar]
  48. Greco, J. P., Greene, J. E., Strauss, M. A., et al. 2018, ApJ, 857, 104 [NASA ADS] [CrossRef] [Google Scholar]
  49. Greene, J.E., Greco, J. P., Goulding, A. D., et al. 2022, ApJ, 933, 150 [NASA ADS] [CrossRef] [Google Scholar]
  50. Haberzettl, L., Bomans, D. J., & Dettmar, R. J. 2007, A&A, 471, 787 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  51. Hayward, C. C., Irwin, J. A., & Bregman, J. N. 2005, ApJ, 635, 827 [NASA ADS] [CrossRef] [Google Scholar]
  52. Hewett, P. C. 1982, MNRAS, 201, 867 [Google Scholar]
  53. Huang, K.-W., Chen, G. C.-F., Chang, P.-W., et al. 2023, in Computer Vision – ECCV 2022 Workshops, ed. L. Karlinsky, T. Michaeli, & K. Nishino (Cham: Springer Nature Switzerland), 143 [Google Scholar]
  54. Impey, C., Bothun, G., & Malin, D. 1988, ApJ, 330, 634 [Google Scholar]
  55. Ivezić, Ž., Kahn, S. M., Tyson, J. A., et al. 2019, ApJ, 873, 111 [Google Scholar]
  56. Jarvis, M. 2015, Astrophysics Source Code Library [record ascl:1508.007] [Google Scholar]
  57. Jia, P., Sun, R., Li, N., et al. 2023, AJ, 165, 26 [NASA ADS] [CrossRef] [Google Scholar]
  58. Junais, Boissier, S., Epinat, B., et al. 2020, A&A, 637, A21 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  59. Junais, Boissier, S., Boselli, A., et al. 2022, A&A, 667, A76 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  60. Kingma, D. P., & Ba, J. 2015, in 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7–9, 2015, Conference Track Proceedings, eds. Y. Bengio, & Y. LeCun [Google Scholar]
  61. Kobayashi, Y., Nishimichi, T., Takada, M., & Miyatake, H. 2022, Phys. Rev. D, 105, 083517 [CrossRef] [Google Scholar]
  62. Koda, J., Yagi, M., Yamanoi, H., & Komiyama, Y. 2015, ApJ, 807, L2 [NASA ADS] [CrossRef] [Google Scholar]
  63. Koo, D. C., & Szalay, A. S. 1984, ApJ, 282, 390 [NASA ADS] [CrossRef] [Google Scholar]
  64. La Marca, A., Iodice, E., Cantiello, M., et al. 2022, A&A, 665, A105 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  65. Landy, S. D., & Szalay, A. S. 1993, ApJ, 412, 64 [Google Scholar]
  66. Laudato, E., & Salzano, V. 2023, Euro. Phys. J. C, 83, 402 [NASA ADS] [CrossRef] [Google Scholar]
  67. Leisman, L., Haynes, M. P., Janowiecki, S., et al. 2017, ApJ, 842, 133 [NASA ADS] [CrossRef] [Google Scholar]
  68. Lim, S., Côté, P., Peng, E. W., et al. 2020, ApJ, 899, 69 [CrossRef] [Google Scholar]
  69. Liutkus, A., Cífka, O., Wu, S., et al. 2021, Proc. Mach. Learn. Res., 139, 7067 [Google Scholar]
  70. Ma, C.-P., & Fry, J. N. 2000, ApJ, 543, 503 [Google Scholar]
  71. Mancera Piña, P. E., Peletier, R. F., Aguerri, J. A. L., et al. 2018, MNRAS, 481, 4381 [Google Scholar]
  72. Mancera Piña, P. E., Aguerri, J. A. L., Peletier, R. F., et al. 2019, MNRAS, 485, 1036 [CrossRef] [Google Scholar]
  73. Marleau, F. R., Habas, R., Poulain, M., et al. 2021, A&A, 654, A105 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  74. Martin, G., Kaviraj, S., Laigle, C., et al. 2019, MNRAS, 485, 796 [NASA ADS] [CrossRef] [Google Scholar]
  75. McConnachie, A. W. 2012, AJ, 144, 4 [Google Scholar]
  76. McGaugh, S. S. 1996, MNRAS, 280, 337 [NASA ADS] [CrossRef] [Google Scholar]
  77. McGaugh, S. S., & Bothun, G. D. 1994, AJ, 107, 530 [NASA ADS] [CrossRef] [Google Scholar]
  78. Mihos, J. C., Durrell, P. R., Ferrarese, L., et al. 2015, ApJ, 809, L21 [Google Scholar]
  79. Mihos, J. C., Harding, P., Feldmeier, J. J., et al. 2017, ApJ, 834, 16 [Google Scholar]
  80. Minchin, R. F., Disney, M. J., Parker, Q. A., et al. 2004, MNRAS, 355, 1303 [NASA ADS] [CrossRef] [Google Scholar]
  81. Mo, H. J., McGaugh, S. S., & Bothun, G. D. 1994, MNRAS, 267, 129 [NASA ADS] [CrossRef] [Google Scholar]
  82. Morganson, E., Gruendl, R. A., Menanteau, F., et al. 2018, PASP, 130, 074501 [NASA ADS] [CrossRef] [Google Scholar]
  83. Neilsen, E. J.Jr., Annis, J. T., Diehl, H. T., et al. 2019, arXiv e-prints [arXiv:1912.06254] [Google Scholar]
  84. Neuschaefer, L. W., Windhorst, R. A., & Dressler, A. 1991, ApJ, 382, 32 [NASA ADS] [CrossRef] [Google Scholar]
  85. O’Neil, K., & Bothun, G. 2000, ApJ, 529, 811 [CrossRef] [Google Scholar]
  86. Parmar, N., Ramachandran, P., Vaswani, A., et al. 2019, in Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, 68 [Google Scholar]
  87. Peacock, J. A., & Smith, R. E. 2000, MNRAS, 318, 1144 [Google Scholar]
  88. Pearson, W. J., Suelves, L. E., Ho, S. C. C., et al. 2022, A&A, 661, A52 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  89. Peebles, P. J. E. 1980, The Large-scale Structure of the Universe (Princeton: Princeton university Press) [Google Scholar]
  90. Peebles, P. J. E., & Hauser, M. G. 1974, ApJS, 28, 19 [NASA ADS] [CrossRef] [Google Scholar]
  91. Peng, C. Y., Ho, L. C., Impey, C. D., & Rix, H.-W. 2002, AJ, 124, 266 [Google Scholar]
  92. Pérez-Carrasco, M., Cabrera-Vives, G., Martinez-Marin, M., et al. 2019, PASP, 131, 108002 [CrossRef] [Google Scholar]
  93. Pimentel, Ó., Estévez, P. A., & Förster, F. 2023, AJ, 165, 18 [NASA ADS] [CrossRef] [Google Scholar]
  94. Poulain, M., Marleau, F. R., Habas, R., et al. 2021, MNRAS, 506, 5494 [NASA ADS] [CrossRef] [Google Scholar]
  95. Prole, D. J., van der Burg, R. F. J., Hilker, M., & Davies, J. I. 2019, MNRAS, 488, 2143 [NASA ADS] [Google Scholar]
  96. Prole, D. J., van der Burg, R. F. J., Hilker, M., & Spitler, L. R. 2021, MNRAS, 500, 2049 [Google Scholar]
  97. Robitaille, T., & Bressert, E. 2012, Astrophysics Source Code Library [record ascl:1208.017] [Google Scholar]
  98. Rojas, K., Savary, E., Clément, B., et al. 2022, A&A, 668, A73 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  99. Román, J., & Trujillo, I. 2017, MNRAS, 468, 4039 [Google Scholar]
  100. Russakovsky, O., Deng, J., Su, H., et al. 2015, Int. J. Comput. Vis., 115, 211 [Google Scholar]
  101. Saburova, A. S., Chilingarian, I. V., Kasparova, A. V., et al. 2021, MNRAS, 503, 830 [NASA ADS] [CrossRef] [Google Scholar]
  102. Saburova, A. S., Chilingarian, I. V., Kulier, A., et al. 2023, MNRAS, 520, L85 [Google Scholar]
  103. Sandage, A., & Binggeli, B. 1984, AJ, 89, 919 [Google Scholar]
  104. Schaefer, C., Geiger, M., Kuntzer, T., & Kneib, J.-P. 2018, A&A, 611, A2 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  105. Schlafly, E. F., & Finkbeiner, D. P. 2011, ApJ, 737, 103 [Google Scholar]
  106. Schlegel, D. J., Finkbeiner, D. P., & Davis, M. 1998, ApJ, 500, 525 [Google Scholar]
  107. Sevilla-Noarbe, I., Bechtol, K., Carrasco Kind, M., et al. 2021, ApJS, 254, 24 [NASA ADS] [CrossRef] [Google Scholar]
  108. Simon, J. D. 2019, ARA&A, 57, 375 [NASA ADS] [CrossRef] [Google Scholar]
  109. Simonyan, K., & Zisserman, A. 2015, in 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7–9, 2015, Conference Track Proceedings, eds. Y. Bengio, & Y. LeCun [Google Scholar]
  110. Sprayberry, D., Impey, C. D., Bothun, G. D., & Irwin, M. J. 1995, AJ, 109, 558 [NASA ADS] [CrossRef] [Google Scholar]
  111. Strateva, I., Ivezić, Ž., Knapp, G. R., et al. 2001, AJ, 122, 1861 [CrossRef] [Google Scholar]
  112. Su, J., Lu, Y., Pan, S., Wen, B., & Liu, Y. 2021, arXiv e-prints [arXiv:2104.09864] [Google Scholar]
  113. Tan, A., Nguyen, D. T., Dax, M., Nießner, M., & Brox, T. 2021, Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021, 9799 [Google Scholar]
  114. Tanoglidis, D., Ćiprijanović, A., & Drlica-Wagner, A. 2021a, Astron. Comput., 35, 100469 [NASA ADS] [CrossRef] [Google Scholar]
  115. Tanoglidis, D., Drlica-Wagner, A., Wei, K., et al. 2021b, ApJS, 252, 18 [Google Scholar]
  116. Thuruthipilly, H., Grespan, M., & Zadrozny, A. 2022a, arXiv e-prints [arXiv:2212.12915] [Google Scholar]
  117. Thuruthipilly, H., Zadrozny, A., Pollo, A., & Biesiada, M. 2022b, A&A, 664, A4 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  118. van der Burg, R. F. J., Muzzin, A., & Hoekstra, H. 2016, A&A, 590, A20 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  119. van Dokkum, P. G., Abraham, R., Merritt, A., et al. 2015, ApJ, 798, L45 [NASA ADS] [CrossRef] [Google Scholar]
  120. 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]
  121. Venhola, A., Peletier, R., Laurikainen, E., et al. 2019, A&A, 625, A143 [EDP Sciences] [Google Scholar]
  122. Wang, X., Kondratyuk, D., Christiansen, E., et al. 2022, in International Conference on Learning Representations [Google Scholar]
  123. Wittmann, C., Lisker, T., Ambachew Tilahun, L., et al. 2017, MNRAS, 470, 1512 [Google Scholar]
  124. Wortsman, M., Ilharco, G., Gadre, S. Y., et al. 2022, in ICML, 23965–23998 [Google Scholar]
  125. Xu, W., Ramos-Ceja, M. E., Pacaud, F., Reiprich, T. H., & Erben, T. 2022, A&A, 658, A59 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  126. Yasuda, N., Fukugita, M., Narayanan, V. K., et al. 2001, AJ, 122, 1104 [NASA ADS] [CrossRef] [Google Scholar]
  127. Yu, J., Wang, Z., Vasudevan, V., et al. 2022, arXiv e-print [arXiv:2205.01917] [Google Scholar]
  128. Zehavi, I., Zheng, Z., Weinberg, D. H., et al. 2005, ApJ, 630, 1 [Google Scholar]
  129. Zhang, H., Goodfellow, I. J., Metaxas, D. N., & Odena, A. 2018, arXiv e-prints [arXiv:1805.08318] [Google Scholar]
  130. Zhao, H., Jia, J., & Koltun, V. 2020, arXiv e-prints [arXiv:2004.13621] [Google Scholar]
  131. Zheng, Z., Berlind, A. A., Weinberg, D. H., et al. 2005, ApJ, 633, 791 [NASA ADS] [CrossRef] [Google Scholar]

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