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Home All issues Volume 701 (September 2025) A&A, 701 (2025) A162 References
Open Access
Issue
A&A
Volume 701, September 2025
Article Number A162
Number of page(s) 22
Section Numerical methods and codes
DOI https://doi.org/10.1051/0004-6361/202452704
Published online 12 September 2025
  1. Aihara, H., Al Sayyad, Y., Ando, M., et al. 2019, PASJ, 71, 114 [Google Scholar]
  2. Aird, J., Coil, A. L., & Georgakakis, A. 2017, MNRAS, 465, 3390 [NASA ADS] [CrossRef] [Google Scholar]
  3. Álvarez-Márquez, J., Colina, L., Marques-Chaves, R., et al. 2019, A&A, 629, A9 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  4. Awang Iskandar, D. N. F., Zijlstra, A. A., McDonald, I., et al. 2020, Galaxies, 8, 88 [NASA ADS] [CrossRef] [Google Scholar]
  5. Baldwin, J. A., Phillips, M. M., & Terlevich, R. 1981, PASP, 93, 5 [Google Scholar]
  6. Baron, D. 2019, arXiv e-prints, [arXiv:1904.07248] [Google Scholar]
  7. Baron, D., & Poznanski, D. 2017, MNRAS, 465, 4530 [NASA ADS] [CrossRef] [Google Scholar]
  8. Bishop, C. M. 2006, Pattern Recognition and Machine Learning (Springer) [Google Scholar]
  9. Bolzonella, M., Miralles, J. M., & Pelló, R. 2000, A&A, 363, 476 [NASA ADS] [Google Scholar]
  10. Bonjean, V., Aghanim, N., Salomé, P., et al. 2019, A&A, 622, A137 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  11. Brescia, M., Cavuoti, S., D’Abrusco, R., Longo, G., & Mercurio, A. 2013, ApJ, 772, 140 [NASA ADS] [CrossRef] [Google Scholar]
  12. Brinchmann, J., Charlot, S., White, S., et al. 2004, MNRAS, 351, 1151 [NASA ADS] [CrossRef] [Google Scholar]
  13. Bruzual, G., & Charlot, S. 2003, MNRAS, 344, 1000 [NASA ADS] [CrossRef] [Google Scholar]
  14. Calzetti, D. 2013, in Secular Evolution of Galaxies, eds. J. Falcón-Barroso, & J. H. Knapen, 419 [Google Scholar]
  15. Carilli, C. L., & Walter, F. 2013, ARA&A, 51, 105 [NASA ADS] [CrossRef] [Google Scholar]
  16. Castro-Ginard, A., Jordi, C., Luri, X., et al. 2018, A&A, 618, A59 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  17. Cavuoti, S., Amaro, V., Brescia, M., et al. 2017, MNRAS, 465, 1959 [Google Scholar]
  18. Cavuoti, S., De Cicco, D., Doorenbos, L., et al. 2024, A&A, 687, A246 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  19. Chu, J., Tang, H., Xu, D., Lu, S., & Long, R. 2024, MNRAS, 528, 6354 [NASA ADS] [CrossRef] [Google Scholar]
  20. Conroy, C. 2013, ARA&A, 51, 393 [NASA ADS] [CrossRef] [Google Scholar]
  21. Conselice, C. J. 2014, ARA&A, 52, 291 [CrossRef] [Google Scholar]
  22. Dark Energy Survey Collaboration (Abbott, T., et al.) 2016, MNRAS, 460, 1270 [Google Scholar]
  23. 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]
  24. Delli Veneri, M., Cavuoti, S., Brescia, M., Longo, G., & Riccio, G. 2019, MNRAS, 486, 1377 [Google Scholar]
  25. Deng, J., Dong, W., Socher, R., et al. 2009, in 2009 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 248 [Google Scholar]
  26. Ding, Y., Sohn, J. H., Kawczynski, M. G., et al. 2019, Radiology, 290, 456 [CrossRef] [Google Scholar]
  27. D’Isanto, A., & Polsterer, K. L. 2018, A&A, 609, A111 [Google Scholar]
  28. Domínguez Sánchez, H., Bernardi, M., & Huertas-Company, M. 2023, Memorie della Societa Astronomica Italiana, 94, 82 [Google Scholar]
  29. Doorenbos, L., Torbaniuk, O., Cavuoti, S., et al. 2022, A&A, 666, A171 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  30. Esteva, A., Kuprel, B., Novoa, R. A., et al. 2017, Nature, 542, 115 [CrossRef] [Google Scholar]
  31. Euclid Collaboration (Desprez, G., et al.) 2020, A&A, 644, A31 [EDP Sciences] [Google Scholar]
  32. Euclid Collaboration (Scaramella, R., et al.) 2022, A&A, 662, A112 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  33. Euclid Collaboration (Kovacˇic´, I., et al.) 2025, A&A, 695, A284 [Google Scholar]
  34. Euclid Collaboration (Mellier, Y., et al.) 2025, A&A, 697, A1 [Google Scholar]
  35. Fabian, A. C. 2012, ARA&A, 50, 455 [Google Scholar]
  36. Fitzpatrick, E. L. 1999, PASP, 111, 63 [Google Scholar]
  37. Fluke, C. J., & Jacobs, C. 2020, WIREs Data Mining Knowledge Discov., 10, e1349 [NASA ADS] [CrossRef] [Google Scholar]
  38. Förster Schreiber, N. M., & Wuyts, S. 2020, ARA&A, 58, 661 [Google Scholar]
  39. Goodfellow, I., Bengio, Y., & Courville, A. 2016, Deep Learning (MIT Press) [Google Scholar]
  40. Green, J., Schechter, P., Baltay, C., et al. 2012, arXiv e-prints, [arXiv:1208.4012] [Google Scholar]
  41. Heckman, T. M., & Best, P. N. 2014, ARA&A, 52, 589 [Google Scholar]
  42. Hickox, R. C., & Alexander, D. M. 2018, ARA&A, 56, 625 [Google Scholar]
  43. Hildebrandt, H., Arnouts, S., Capak, P., et al. 2010, A&A, 523, A31 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  44. Humphrey, A., Cunha, P. A. C., Paulino-Afonso, A., et al. 2023, MNRAS, 520, 305 [NASA ADS] [CrossRef] [Google Scholar]
  45. Ilbert, O., Arnouts, S., McCracken, H. J., et al. 2006, A&A, 457, 841 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  46. Ivezic´, Ž., Kahn, S. M., Tyson, J. A., et al. 2019, ApJ, 873, 111 [NASA ADS] [CrossRef] [Google Scholar]
  47. Kauffmann, G., Heckman, T. M., Tremonti, C., et al. 2003a, MNRAS, 346, 1055 [Google Scholar]
  48. Kauffmann, G., Heckman, T. M., White, S. D. M., et al. 2003b, MNRAS, 341, 33 [Google Scholar]
  49. Kennicutt, R. C., & Evans, N.J. 2012, ARA&A, 50, 531 [NASA ADS] [CrossRef] [Google Scholar]
  50. Kim, D.-W., Protopapas, P., Byun, Y.-I., et al. 2011, ApJ, 735, 68 [NASA ADS] [CrossRef] [Google Scholar]
  51. Kim, Y. J., Bae, J. P., Chung, J.-W., et al. 2021, Sci. Rep., 11, 3605 [NASA ADS] [CrossRef] [Google Scholar]
  52. Kinson, D. A., Oliveira, J. M., & van Loon, J. T. 2021, MNRAS, 507, 5106 [NASA ADS] [CrossRef] [Google Scholar]
  53. Kormendy, J., & Ho, L. C. 2013, ARA&A, 51, 511 [Google Scholar]
  54. Koyama, Y., Kodama, T., Hayashi, M., et al. 2015, MNRAS, 453, 879 [Google Scholar]
  55. Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. 1998, Proc. IEEE, 86, 2278 [Google Scholar]
  56. Lin, Z., & Yan, R. 2024, A&A, 691, A201 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  57. Longo, G., Merényi, E., & Tinˇo, P. 2019, PASP, 131, 100101 [NASA ADS] [CrossRef] [Google Scholar]
  58. Madau, P., & Dickinson, M. 2014, ARA&A, 52, 415 [Google Scholar]
  59. Magnier, E. A., Schlafly, E. F., Finkbeiner, D. P., et al. 2020, ApJS, 251, 6 [NASA ADS] [CrossRef] [Google Scholar]
  60. Maiolino, R., & Mannucci, F. 2019, A&A Rev., 27, 3 [NASA ADS] [CrossRef] [Google Scholar]
  61. Martinazzo, A., Espadoto, M., & Hirata, N. S. 2021, in 2020 25th International Conference on Pattern Recognition (ICPR), IEEE, 4176 [Google Scholar]
  62. Masters, D., Capak, P., Stern, D., et al. 2015, ApJ, 813, 53 [Google Scholar]
  63. Menegola, A., Fornaciali, M., Pires, R., et al. 2017, in 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), IEEE, 297 [Google Scholar]
  64. Morganti, R., & Oosterloo, T. 2018, A&A Rev., 26, 4 [Google Scholar]
  65. Pasquet, J., Bertin, E., Treyer, M., Arnouts, S., & Fouchez, D. 2019, A&A, 621, A26 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  66. Pathi, I. M., Soo, J. Y. H., Wee, M. J., et al. 2025, J. Cosmology Astropart. Phys., 2025, 097 [Google Scholar]
  67. Prima, B., & Bouhorma, M. 2020, ISPRS – International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 4443, 343 [Google Scholar]
  68. Qin, J., Zheng, X. Z., Wuyts, S., Pan, Z., & Ren, J. 2019, ApJ, 886, 28 [NASA ADS] [CrossRef] [Google Scholar]
  69. Razim, O., Cavuoti, S., Brescia, M., et al. 2021, MNRAS, 507, 5034 [NASA ADS] [CrossRef] [Google Scholar]
  70. Salim, S., Rich, R. M., Charlot, S., et al. 2007, ApJS, 173, 267 [NASA ADS] [CrossRef] [Google Scholar]
  71. Schaefer, C., Geiger, M., Kuntzer, T., & Kneib, J.-P. 2018, A&A, 611, A2 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  72. Schmidhuber, J. 2015, Neural Netw., 61, 85 [CrossRef] [Google Scholar]
  73. Soo, J. Y. H., Moraes, B., Joachimi, B., et al. 2018, MNRAS, 475, 3613 [NASA ADS] [CrossRef] [Google Scholar]
  74. Surana, S., Wadadekar, Y., Bait, O., & Bhosale, H. 2020, MNRAS, 493, 4808 [NASA ADS] [CrossRef] [Google Scholar]
  75. Tan, M., & Le, Q. 2019, in International Conference on Machine Learning, PMLR, 6105 [Google Scholar]
  76. Torbaniuk, O., Paolillo, M., D’Abrusco, R., et al. 2024, MNRAS, 527, 12091 [Google Scholar]
  77. Tremonti, C. A., Heckman, T. M., Kauffmann, G., et al. 2004, ApJ, 613, 898 [Google Scholar]
  78. Wang, Y., Yao, Q., Kwok, J. T., &Ni, L. M. 2020, ACM Comput. Surv., 53, 1 [Google Scholar]
  79. Way, M. J., Foster, L. V., Gazis, P. R., & Srivastava, A. N. 2009, ApJ, 706, 623 [Google Scholar]
  80. Wenzl, L., Schindler, J.-T., Fan, X., et al. 2021, AJ, 162, 72 [NASA ADS] [CrossRef] [Google Scholar]
  81. Willett, K. W., Lintott, C. J., Bamford, S. P., et al. 2013, MNRAS, 435, 2835 [Google Scholar]
  82. York, D. G., Adelman, J., Anderson, Jr., J. E., et al. 2000, AJ, 120, 1579 [NASA ADS] [CrossRef] [Google Scholar]
  83. Zeraatgari, F. Z., Hafezianzadeh, F., Zhang, Y. X., Mosallanezhad, A., & Zhang, J. Y. 2024, A&A, 688, A33 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  84. Zhong, J., Deng, Z., Li, X., et al. 2024, MNRAS, 531, 2011 [CrossRef] [Google Scholar]
  85. Zou, M., & Zhong, Y. 2018, Sensing Imaging, 19, 6 [NASA ADS] [CrossRef] [Google Scholar]

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