Open Access
Issue
A&A
Volume 703, November 2025
Article Number A276
Number of page(s) 24
Section Numerical methods and codes
DOI https://doi.org/10.1051/0004-6361/202554065
Published online 26 November 2025
  1. Acquaviva, V. 2016, MNRAS, 456, 1618 [NASA ADS] [CrossRef] [Google Scholar]
  2. Alam, S., Albareti, F. D., Allende Prieto, C., et al. 2015, ApJS, 219, 12 [Google Scholar]
  3. Baldwin, J. A., Phillips, M. M., & Terlevich, R. 1981, PASP, 93, 5 [Google Scholar]
  4. Bartlett, S., Li, J., Gu, L., et al. 2022, Nat. Astron., 6, 387 [Google Scholar]
  5. Berrevoets, J., Kacprzyk, K., Qian, Z., & van der Schaar, M. 2023, arXiv e-prints [arXiv:2303.02186] [Google Scholar]
  6. Blanton, M. R., Schlegel, D. J., Strauss, M. A., et al. 2005, AJ, 129, 2562 [NASA ADS] [CrossRef] [Google Scholar]
  7. Bonjean, V., Aghanim, N., Salomé, P., et al. 2019, A&A, 622, A137 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  8. Brinchmann, J., Charlot, S., White, S. D. M., et al. 2004, MNRAS, 351, 1151 [Google Scholar]
  9. Buck, T., & Wolf, S. 2021, arXiv e-prints [arXiv:2111.01154] [Google Scholar]
  10. Chu, J., Tang, H., Xu, D., Lu, S., & Long, R. 2024, MNRAS, 528, 6354 [NASA ADS] [CrossRef] [Google Scholar]
  11. Conroy, C., Gunn, J. E., & White, M. 2009, ApJ, 699, 486 [Google Scholar]
  12. Davidzon, I., Jegatheesan, K., Ilbert, O., et al. 2022, A&A, 665, A34 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  13. de Avellar, M., & Horvath, J. 2012, Phys. Lett. A, 376, 1085 [Google Scholar]
  14. Delli Veneri, M., Cavuoti, S., Brescia, M., Longo, G., & Riccio, G. 2019, MNRAS, 486, 1377 [Google Scholar]
  15. Deng, Z., Zheng, X., Tian, H., & Dajun Zeng, D. 2022, arXiv e-prints [arXiv:2211.03374] [Google Scholar]
  16. D’Isanto, A., Cavuoti, S., Gieseke, F., & Polsterer, K. L. 2018, A&A, 616, A97 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  17. Dobbels, W., Krier, S., Pirson, S., et al. 2019, A&A, 624, A102 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  18. Euclid Collaboration (Bisigello, L., et al.) 2023, MNRAS, 520, 3529 [NASA ADS] [CrossRef] [Google Scholar]
  19. Euclid Collaboration (Humphrey, A., et al.) 2023, A&A, 671, A99 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  20. García-Alvarado, M. V., Li, X. D., & Forero-Romero, J. E. 2020, MNRAS, 498, L145 [Google Scholar]
  21. Gebhard, T. D., Bonse, M. J., Quanz, S. P., & Schölkopf, B. 2022, A&A, 666, A9 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  22. Gilbert, G. J., & Fabrycky, D. C. 2020, AJ, 159, 281 [Google Scholar]
  23. Heinze-Deml, C., Maathuis, M. H., & Meinshausen, N. 2018, Annu. Rev. Statist. Appl., 5, 371 [Google Scholar]
  24. Hoyle, B., Rau, M. M., Zitlau, R., Seitz, S., & Weller, J. 2015, MNRAS, 449, 1275 [Google Scholar]
  25. Ivezic, Ž., Kahn, S. M., Tyson, J. A., et al. 2019, ApJ, 873, 111 [NASA ADS] [CrossRef] [Google Scholar]
  26. Jin, Z., Pasquato, M., Davis, B. L., et al. 2025, ApJ, 979, 212 [Google Scholar]
  27. Jones, E., & Singal, J. 2017, A&A, 600, A113 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  28. Kaddour, J., Lynch, A., Liu, Q., Kusner, M. J., & Silva, R. 2022, arXiv e-prints [arXiv:2206.15475] [Google Scholar]
  29. Kauffmann, G., Heckman, T. M., White, S. D. M., et al. 2003, MNRAS, 341, 33 [Google Scholar]
  30. 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, ed. Y. Bengio & Y. LeCun [Google Scholar]
  31. La Torre, V., Sajina, A., Goulding, A. D., et al. 2024, AJ, 167, 261 [Google Scholar]
  32. Laureijs, R., Amiaux, J., Arduini, S., et al. 2011, arXiv e-prints [arXiv:1110.3193] [Google Scholar]
  33. Lin, Q., Ruan, H., Fouchez, D., et al. 2024, A&A, 691, A331 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  34. Lu, J., Luo, Z., Chen, Z., et al. 2024, MNRAS, 527, 12140 [Google Scholar]
  35. Lundberg, S. M., & Lee, S.-I. 2017, in Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS'17 (Red Hook, NY, USA: Curran Associates Inc.), 4768 [Google Scholar]
  36. Maller, A. H., Berlind, A. A., Blanton, M. R., & Hogg, D. W. 2009, ApJ, 691, 394 [NASA ADS] [CrossRef] [Google Scholar]
  37. Marta Pinho, A., Reischke, R., Teich, M., & Schäfer, B. M. 2021, MNRAS, 503, 1187 [Google Scholar]
  38. Martínez-Sánchez, Á., Arranz, G., & Lozano-Durán, A. 2024, Nat. Commun., 15, 9296 [Google Scholar]
  39. Montavon, G., Lapuschkin, S., Binder, A., Samek, W., & Müller, K.-R. 2017, Pattern Recogn., 65, 211 [Google Scholar]
  40. Mucesh, S., Hartley, W. G., Palmese, A., et al. 2021, MNRAS, 502, 2770 [NASA ADS] [CrossRef] [Google Scholar]
  41. Mucesh, S., Hartley, W. G., Gilligan-Lee, C. M., & Lahav, O. 2024, arXiv e-prints [arXiv:2412.02439] [Google Scholar]
  42. Pandey, B. 2016, MNRAS, 463, 4239 [Google Scholar]
  43. Pandey, B., & Sarkar, S. 2016, MNRAS, 460, 1519 [Google Scholar]
  44. Pasquato, M., Jin, Z., Lemos, P., Davis, B. L., & Macciò, A. V. 2023, arXiv e-prints [arXiv:2311.15160] [Google Scholar]
  45. Pasquet, J., Bertin, E., Treyer, M., Arnouts, S., & Fouchez, D. 2019, A&A, 621, A26 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  46. Pearl, J. 2009, Causality, 2nd edn. (Cambridge University Press) [Google Scholar]
  47. Ribeiro, M. T., Singh, S., & Guestrin, C. 2016, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16 (New York, NY, USA: Association for Computing Machinery), 11351144 [Google Scholar]
  48. Salim, S., Rich, R. M., Charlot, S., et al. 2007, ApJS, 173, 267 [NASA ADS] [CrossRef] [Google Scholar]
  49. Schlegel, D. J., Finkbeiner, D. P., & Davis, M. 1998, ApJ, 500, 525 [Google Scholar]
  50. Schölkopf, B., Hogg, D. W., Wang, D., et al. 2016, PNAS, 113, 7391 [CrossRef] [Google Scholar]
  51. Schlkopf, B., Locatello, F., Bauer, S., et al. 2021, Proc. IEEE, 109, 612 [Google Scholar]
  52. Segal, G., Parkinson, D., & Bartlett, S. 2024, AJ, 167, 114 [Google Scholar]
  53. Shannon, C. E. 1948, Bell Syst. Tech. J., 27, 379 [Google Scholar]
  54. Shrikumar, A., Greenside, P., & Kundaje, A. 2017, in Proceedings of the 34th International Conference on Machine Learning - Volume 70, ICML’17 (JMLR.org), 3145 [Google Scholar]
  55. Simonyan, K., Vedaldi, A., & Zisserman, A. 2013, arXiv e-prints [arXiv: 1312.6034] [Google Scholar]
  56. Singal, J., Shmakova, M., Gerke, B., Griffith, R. L., & Lotz, J. 2011, PASP, 123, 615 [NASA ADS] [CrossRef] [Google Scholar]
  57. Soo, J. Y. H., Moraes, B., Joachimi, B., et al. 2018, MNRAS, 475, 3613 [NASA ADS] [CrossRef] [Google Scholar]
  58. Spergel, D., Gehrels, N., Baltay, C., et al. 2015, arXiv e-prints [arXiv: 1503.03757] [Google Scholar]
  59. Surana, S., Wadadekar, Y., Bait, O., & Bhosale, H. 2020, MNRAS, 493, 4808 [NASA ADS] [CrossRef] [Google Scholar]
  60. Ting, Y.-S., & Weinberg, D. H. 2022, ApJ, 927, 209 [NASA ADS] [CrossRef] [Google Scholar]
  61. Treyer, M., Ait Ouahmed, R., Pasquet, J., et al. 2024, MNRAS, 527, 651 [Google Scholar]
  62. Vannah, S., Gleiser, M., & Kaltenegger, L. 2024, MNRAS, 528, L4 [Google Scholar]
  63. Vazza, F. 2017, MNRAS, 465, 4942 [Google Scholar]
  64. Wang, D., Hogg, D. W., Foreman-Mackey, D., & Schölkopf, B. 2016, PASP, 128, 094503 [Google Scholar]
  65. Way, M. J., Foster, L. V., Gazis, P. R., & Srivastava, A. N. 2009, ApJ, 706, 623 [Google Scholar]
  66. Williams, P. L., & Beer, R. D. 2010, arXiv e-prints [arXiv: 1004.2515] [Google Scholar]
  67. Wright, E. L., Eisenhardt, P. R. M., Mainzer, A. K., et al. 2010, AJ, 140, 1868 [Google Scholar]
  68. Wu, J. F., & Boada, S. 2019, MNRAS, 484, 4683 [NASA ADS] [CrossRef] [Google Scholar]
  69. Yao, L., Chu, Z., Li, S., et al. 2021, ACM Trans. Knowl. Discov. Data, 15 [Google Scholar]
  70. Yip, C.-W., Szalay, A. S., Carliles, S., & Budavári, T. 2011, ApJ, 730, 54 [Google Scholar]
  71. York, D. G., Adelman, J., Anderson, Jr., J. E., et al. 2000, AJ, 120, 1579 [NASA ADS] [CrossRef] [Google Scholar]
  72. 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]
  73. Zhan, H. 2018, in 42nd COSPAR Scientific Assembly, 42, E1.16 [Google Scholar]
  74. Zhong, J., Deng, Z., Li, X., et al. 2024, MNRAS, 531, 2011 [CrossRef] [Google Scholar]
  75. Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. 2016, in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2921 [Google Scholar]

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