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Home All issues Volume 706 (February 2026) A&A, 706 (2026) A317 References
Open Access
Issue
A&A
Volume 706, February 2026
Article Number A317
Number of page(s) 13
Section Extragalactic astronomy
DOI https://doi.org/10.1051/0004-6361/202554654
Published online 18 February 2026
  1. Abadi, M., Agarwal, A., Barham, P., et al. 2015, TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, software available from tensorflow.org [Google Scholar]
  2. Agarap, A. F. 2018, arXiv e-prints [arXiv:1803.08375] [Google Scholar]
  3. Ascaso, B., Aguerri, J. A. L., Varela, J., et al. 2010, ApJ, 726, 69 [Google Scholar]
  4. Bai, L., Yee, H., Yan, R., et al. 2014, ApJ, 789, 134 [Google Scholar]
  5. Baron, D. 2019, Machine Learning in Astronomy: a practical overview [Google Scholar]
  6. Baum, W. A. 1959, PASP, 71, 106 [Google Scholar]
  7. Belfiore, F., Ginolfi, M., Blanc, G., et al. 2025, Machine learning the gap between real and simulated nebulae: A domain-adaptation approach to classify ionised nebulae in nearby galaxies [Google Scholar]
  8. Bernardi, M. 2009, MNRAS, 395, 1491 [Google Scholar]
  9. Bertin, E., & Arnouts, S. 1996, A&AS, 117, 393 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  10. Biermann, P., & Tinsley, B. M. 1975, A&A, 41, 441 [NASA ADS] [Google Scholar]
  11. Bosch, J., Armstrong, R., Bickerton, S., et al. 2018, PASJ, 70, S5 [Google Scholar]
  12. Bosch, J., AlSayyad, Y., Armstrong, R., et al. 2019, ASP Conf. Ser., 523, 521 [Google Scholar]
  13. Bruzual, G., & Charlot, S. 2003, MNRAS, 344, 1000 [NASA ADS] [CrossRef] [Google Scholar]
  14. Burke, C. J., Aleo, P. D., Chen, Y.-C., et al. 2019, MNRAS, 490, 3952 [NASA ADS] [CrossRef] [Google Scholar]
  15. Canepa, L., Brough, S., Lanusse, F., Montes, M., & Hatch, N. 2025, Measuring the intracluster light fraction with machine learning [Google Scholar]
  16. Cho, J., Lee, K., Shin, E., Choy, G., & Do, S. 2015, arXiv e-prints [arXiv:1511.06348] [Google Scholar]
  17. Chu, A., Durret, F., & Márquez, I. 2021, A&A, 649, A42 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  18. Chu, A., Sarron, F., Durret, F., & Márquez, I. 2022, A&A, 666, A54 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  19. De Lucia, G., Springel, V., White, S. D. M., Croton, D., & Kauffmann, G. 2006, MNRAS, 366, 499 [NASA ADS] [CrossRef] [Google Scholar]
  20. De Propris, R., West, M. J., Andrade-Santos, F., et al. 2020, MNRAS, 500, 310 [Google Scholar]
  21. de Vaucouleurs, G. 1961, ApJS, 5, 233 [NASA ADS] [CrossRef] [Google Scholar]
  22. DeMaio, T., Gonzalez, A. H., Zabludoff, A., et al. 2020, MNRAS, 491, 3751 [Google Scholar]
  23. Dressler, A. 1980, ApJ, 236, 351 [Google Scholar]
  24. Dressler, A. 1984, ARA&A, 22, 185 [NASA ADS] [CrossRef] [Google Scholar]
  25. Dubois-Felsmann, G., Economou, F., Lim, K.-T., et al. 2019, https://ldm-542.lsst.io [Google Scholar]
  26. Durret, F., Tarricq, Y., Márquez, I., Ashkar, H., & Adami, C. 2019, A&A, 622, A78 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  27. Edwards, L. O. V., Salinas, M., Stanley, S., et al. 2019, MNRAS, 491, 2617 [Google Scholar]
  28. Golden-Marx, J. B., Miller, C. J., Zhang, Y., et al. 2022, ApJ, 928, 28 [NASA ADS] [CrossRef] [Google Scholar]
  29. Hashimoto, Y., Henry, J. P., & Boehringer, H. 2014, MNRAS, 440, 588 [Google Scholar]
  30. He, K., Zhang, X., Ren, S., & Sun, J. 2015, Deep Residual Learning for Image Recognition [Google Scholar]
  31. Heitmann, K., Finkel, H., Pope, A., et al. 2019, ApJS, 245, 16 [NASA ADS] [CrossRef] [Google Scholar]
  32. Hickson, P., Richstone, D. O., & Turner, E. L. 1977, ApJ, 213, 323 [Google Scholar]
  33. Hubble, E. P. 1936, Realm of the Nebulae [Google Scholar]
  34. Huertas-Company, M., & Lanusse, F. 2023, PASA, 40, e001 [NASA ADS] [CrossRef] [Google Scholar]
  35. Ivezić, Ž., Kahn, S. M., Tyson, J. A., et al. 2019, ApJ, 873, 111 [Google Scholar]
  36. Janulewicz, P., Webb, T. M. A., & Perreault-Levasseur, L. 2025, ApJ, 981, 117 [Google Scholar]
  37. Jenness, T., Bosch, J. F., Salnikov, A., et al. 2022, SPIE Conf. Ser., 12189, 1218911 [Google Scholar]
  38. Jurić, M., Ciardi, D. R., Dubois-Felsmann, G. P., & Guy, L. P. 2019, https://lse-319.lsst.io [Google Scholar]
  39. Kingma, D. P., & Ba, J. 2017, Adam: A Method for Stochastic Optimization [Google Scholar]
  40. Korytov, D., Hearin, A., Kovacs, E., et al. 2019, ApJS, 245, 26 [NASA ADS] [CrossRef] [Google Scholar]
  41. Larson, R. B. 1975, MNRAS, 173, 671 [NASA ADS] [CrossRef] [Google Scholar]
  42. Lauer, T. R., Postman, M., Strauss, M. A., Graves, G. J., & Chisari, N. E. 2014, ApJ, 797, 82 [Google Scholar]
  43. Lietzen, H., Tempel, E., Heinämäki, P., et al. 2012, A&A, 545, A104 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  44. LSST Dark Energy Science Collaboration (Abolfathi, B., et al.) 2022, DESC DC2 Data Release Note [Google Scholar]
  45. Marchant, A. B., & Shapiro, S. L. 1977, ApJ, 215, 1 [Google Scholar]
  46. Marini, I., Borgani, S., Saro, A., et al. 2022, MNRAS, 514, 3082 [NASA ADS] [CrossRef] [Google Scholar]
  47. Milletari, F., Navab, N., & Ahmadi, S.-A. 2016, V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation [Google Scholar]
  48. Moriwaki, K., Nishimichi, T., & Yoshida, N. 2023, Rep. Prog. Phys., 86, 076901 [NASA ADS] [CrossRef] [Google Scholar]
  49. Nelson, A. E., Simard, L., Zaritsky, D., Dalcanton, J. J., & Gonzalez, A. H. 2002, ApJ, 567, 144 [NASA ADS] [CrossRef] [Google Scholar]
  50. Ntampaka, M., Avestruz, C., Boada, S., et al. 2021, The Role of Machine Learning in the Next Decade of Cosmology [Google Scholar]
  51. Oemler, A. J. 1974, ApJ, 194, 1 [NASA ADS] [CrossRef] [Google Scholar]
  52. O’Mullane, W., Economou, F., Huang, F., et al. 2024, ASP Conf. Ser., 535, 227 [Google Scholar]
  53. Partridge, R. B., & Peebles, P. J. E. 1967, ApJ, 147, 868 [Google Scholar]
  54. Patel, P., Maddox, S., Pearce, F. R., Aragon-Salamanca, A., & Conway, E. 2006, MNRAS, 370, 851 [Google Scholar]
  55. Pearson, W. J., Santos, D. J. D., Goto, T., et al. 2024, A&A, 686, A94 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  56. Postman, M., & Geller, M. J. 1984, ApJ, 281, 95 [Google Scholar]
  57. Querejeta, M., Eliche-Moral, M. C., Tapia, T., et al. 2015, A&A, 579, L2 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  58. Rood, H. J., Page, T. L., Kintner, E. C., & King, I. R. 1972, ApJ, 175, 627 [Google Scholar]
  59. Roos, N., & Norman, C. A. 1979, A&A, 76, 75 [NASA ADS] [Google Scholar]
  60. Rykoff, E. S., Rozo, E., Busha, M. T., et al. 2014, ApJ, 785, 104 [Google Scholar]
  61. Sawala, T., Frenk, C., Jasche, J., Johansson, P. H., & Lavaux, G. 2023, Distinct distributions of elliptical and disk galaxies across the Local Supercluster as a ΛCDM prediction [Google Scholar]
  62. Sohn, J., Geller, M. J., Diaferio, A., & Rines, K. J. 2020, ApJ, 891, 129 [NASA ADS] [CrossRef] [Google Scholar]
  63. Sohn, J., Geller, M. J., Vogelsberger, M., & Damjanov, I. 2022, ApJ, 931, 31 [NASA ADS] [CrossRef] [Google Scholar]
  64. Spitzer, L. J., & Baade, W. 1951, ApJ, 113, 413 [NASA ADS] [CrossRef] [Google Scholar]
  65. Stopyra, S., Peiris, H. V., Pontzen, A., Jasche, J., & Natarajan, P. 2021, MNRAS, 507, 5425 [Google Scholar]
  66. Stott, J. P., Collins, C. A., Burke, C., Hamilton-Morris, V., & Smith, G. P. 2011, MNRAS, 414, 445 [Google Scholar]
  67. Sureshkumar, U., Durkalec, A., Pollo, A., et al. 2024, A&A, 686, A40 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  68. Takeda, Y., Kashikawa, N., Ito, K., et al. 2024, ApJ, 977, 81 [Google Scholar]
  69. The LSST Dark Energy Science Collaboration (Abolfathi, B., et al.) 2021, ApJS, 253, 31 [CrossRef] [Google Scholar]
  70. Toomre, A. 1977, in Evolution of Galaxies and Stellar Populations, eds. B. M. Tinsley, D. C. Larson, & R. B. Gehret, 401 [Google Scholar]
  71. West, M. J., de Propris, R., Bremer, M. N., & Phillipps, S. 2017, Nat. Astron., 1, 0157 [Google Scholar]
  72. Yang, L., Silverman, J., Oguri, M., et al. 2024, MNRAS, 531, 4006 [Google Scholar]

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