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Home All issues Volume 701 (September 2025) A&A, 701 (2025) A150 References
Open Access
Issue
A&A
Volume 701, September 2025
Article Number A150
Number of page(s) 27
Section Numerical methods and codes
DOI https://doi.org/10.1051/0004-6361/202554025
Published online 12 September 2025
  1. Abdurro’uf, Accetta, K., Aerts, C., et al. 2022, ApJS, 259, 35 [NASA ADS] [CrossRef] [Google Scholar]
  2. Aerts, C. 2021, Rev. Mod. Phys., 93, 015001 [Google Scholar]
  3. Aerts, C., & Tkachenko, A. 2024, A&A, 692, R1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  4. Bahdanau, D., Cho, K., & Bengio, Y. 2014, in International Conference on Learning Representations 2015 [arXiv:1409.0473] [Google Scholar]
  5. Bellm, E. C., Kulkarni, S. R., Graham, M. J., et al. 2019, PASP, 131, 018002 [Google Scholar]
  6. Bengio, Y., Courville, A., & Vincent, P. 2013, IEEE Trans. Pattern Anal. Mach. Intell., 35, 1798 [CrossRef] [Google Scholar]
  7. Blagorodnova, N., Neill, J. D., Walters, R., et al. 2018, PASP, 130, 035003 [Google Scholar]
  8. Blažko, S. 1907, Astron. Nachr., 175, 325 [Google Scholar]
  9. Breiman, L. 2001, Mach. Learn., 45, 5 [Google Scholar]
  10. Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. 2000, SIGMOD Rec., 29, 93 [Google Scholar]
  11. Briquet, M., Hubrig, S., De Cat, P., et al. 2007, A&A, 466, 269 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  12. Buck, T., & Schwarz, C. 2024, in NIPS 2024 Workshop on Machine Learning and the Physical Sciences [arXiv:2410.16081] [Google Scholar]
  13. Burgess, C. P., Higgins, I., Pal, A., et al. 2018, in NIPS 2017 Workshop on Learning Disentangled Representations [arXiv:1804.03599] [Google Scholar]
  14. Carrasco, J. M., Weiler, M., Jordi, C., et al. 2021, A&A, 652, A86 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  15. Carnerero, M. I., Raiteri, C. M., Rimoldini, L., et al. 2023, A&A, 674, A24 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  16. Catelan, M. 2009, Ap&SS, 320, 261 [Google Scholar]
  17. Catelan, M., & Smith, H. A. 2015, Pulsating Stars (John Wiley & Sons) [Google Scholar]
  18. Chen, T., & Guestrin, C. 2016, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’16, 785 [Google Scholar]
  19. Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. 2014, in NIPS 2014 Deep Learning and Representation Learning Workshop [arXiv:1412.3555] [Google Scholar]
  20. Clementini, G., Ripepi, V., Garofalo, A., et al. 2023, A&A, 674, A18 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  21. Cui, X.-Q., Zhao, Y.-H., Chu, Y.-Q., et al. 2012, Res. Astron. Astrophys., 12, 1197 [Google Scholar]
  22. Davies, D. L., & Bouldin, D. W. 1979, IEEE Trans. Pattern Anal. Mach. Intell., PAMI-1, 224 [Google Scholar]
  23. De Angeli, F., Weiler, M., Montegriffo, P., et al. 2023, A&A, 674, A2 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  24. DESI Collaboration (Adame, A. G., et al.) 2024, AJ, 168, 58 [NASA ADS] [CrossRef] [Google Scholar]
  25. Donoso-Oliva, C., Becker, I., Protopapas, P., et al. 2023, A&A, 670, A54 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  26. Evans, D. W., Eyer, L., Busso, G., et al. 2023, A&A, 674, A4 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  27. Eyer, L., & Mowlavi, N. 2008, in Journal of Physics Conference Series, 118, 012010 [Google Scholar]
  28. Eyer, L., Audard, M., Holl, B., et al. 2023, A&A, 674, A13 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  29. Falcon, W., Borovec, J., Wälchli, A., et al. 2020, https://zenodo.org/record/3828935 [Google Scholar]
  30. Fritzewski, D. J., Vanrespaille, M., Aerts, C., et al. 2025, A&A, 698, A253 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  31. Gaia Collaboration (Prusti, T., et al.) 2016, A&A, 595, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  32. Gaia Collaboration (De Ridder, J., et al.) 2023a, A&A, 674, A36 [CrossRef] [EDP Sciences] [Google Scholar]
  33. Gaia Collaboration (Vallenari, A., et al.) 2023b, A&A, 674, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  34. Garrison, L. H., Foreman-Mackey, D., Shih, Y.-h., & Barnett, A. 2024, RNAAS, 8, 250 [Google Scholar]
  35. Gavras, P., Rimoldini, L., Nienartowicz, K., et al. 2023, A&A, 674, A22 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  36. Ginsburg, A., Sipőcz, B. M., Brasseur, C. E., et al. 2019, AJ, 157, 98 [Google Scholar]
  37. Glorot, X., Bordes, A., & Bengio, Y. 2011, in Proceedings of Machine Learning Research, 15, Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (PMLR), 315 [Google Scholar]
  38. Goodfellow, I., Bengio, Y., & Courville, A. 2016, Deep Learning (The MIT Press) [Google Scholar]
  39. Gui, J., Chen, T., Zhang, J., et al. 2024, IEEE Trans. Pattern Anal. Mach. Intell., 46, 9052 [Google Scholar]
  40. Guiglion, G., Nepal, S., Chiappini, C., et al. 2024, A&A, 682, A9 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  41. Guo, W., Wang, J., & Wang, S. 2019, IEEE Access, 7, 63373 [CrossRef] [Google Scholar]
  42. He, K., Zhang, X., Ren, S., & Sun, J. 2015, in Proceedings of the IEEE International Conference on Computer Vision (ICCV), IEEE, 1026 [Google Scholar]
  43. Heinze, A. N., Tonry, J. L., Denneau, L., et al. 2018, AJ, 156, 241 [Google Scholar]
  44. Hendrycks, D., & Gimpel, K. 2016, arXiv e-prints [arXiv:1606.08415] [Google Scholar]
  45. Hey, D., & Aerts, C. 2024, A&A, 688, A93 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  46. Hochreiter, S., Schmidhuber, J., & Elvezia, C. 1997, Neural Computat., 9, 1735 [Google Scholar]
  47. Holl, B., Fabricius, C., Portell, J., et al. 2023, A&A, 674, A25 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  48. Hu, W., & Tan, Y. 2018, in AAAI Workshops, 245 [Google Scholar]
  49. Huertas-Company, M., Sarmiento, R., & Knapen, J. H. 2023, RAS Tech. Instrum., 2, 441 [NASA ADS] [CrossRef] [Google Scholar]
  50. Jamal, S., & Bloom, J. S. 2020, ApJS, 250, 30 [NASA ADS] [CrossRef] [Google Scholar]
  51. Jayasinghe, T., Stanek, K. Z., Kochanek, C. S., et al. 2019, MNRAS, 486, 1907 [NASA ADS] [Google Scholar]
  52. Jiang, Z., Zheng, Y., Tan, H., Tang, B., & Zhou, H. 2017, in Proceedings of the 26th International Joint Conference on Artificial Intelligence, 1965 [Google Scholar]
  53. Kingma, D. P., & Ba, J. 2015, in International Conference on Learning Representations (ICLR) [arXiv:1412.6980] [Google Scholar]
  54. Kingma, D. P., & Welling, M. 2014, in International Conference on Learning Representations (ICLR) [arXiv:1312.6114] [Google Scholar]
  55. Laroche, A., & Speagle, J. S. 2023, in International Conference in Machine Learning, Workshop on Machine Learning for Astrophysics [arXiv:2307.06378] [Google Scholar]
  56. Lebzelter, T., Mowlavi, N., Lecoeur-Taibi, I., et al. 2023, A&A, 674, A15 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  57. Lei Ba, J., Kiros, J. R., & Hinton, G. E. 2016, arXiv e-prints [arXiv:1607.06450] [Google Scholar]
  58. Liu, X., Zhang, F., Hou, Z., et al. 2021, IEEE Trans. Knowledge Data Eng., 35, 857 [Google Scholar]
  59. Lomb, N. R. 1976, Ap&SS, 39, 447 [Google Scholar]
  60. Luongo, E., Ripepi, V., Marconi, M., et al. 2024, A&A, 690, L17 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  61. Mahabal, A., Sheth, K., Gieseke, F., et al. 2017, in 2017 IEEE symposium Series on Computational Intelligence (SSCI), IEEE, 1 [Google Scholar]
  62. Martínez-Palomera, J., Bloom, J. S., & Abrahams, E. S. 2022, AJ, 164, 263 [CrossRef] [Google Scholar]
  63. Mombarg, J. S. G., Aerts, C., Van Reeth, T., & Hey, D. 2024, A&A, 691, A131 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  64. Mowlavi, N., Holl, B., Lecoeur-Taïbi, I., et al. 2023, A&A, 674, A16 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  65. Nair, V., & Hinton, G. E. 2010, in Proceedings of the 27th International Conference on Machine Learning (ICML-10), 807 [Google Scholar]
  66. Naul, B., Bloom, J. S., Pérez, F., & van der Walt, S. 2018, Nat. Astron., 2, 151 [NASA ADS] [CrossRef] [Google Scholar]
  67. Oosterhoff, P. T. 1939, The Observatory, 62, 104 [NASA ADS] [Google Scholar]
  68. Oosterhoff, P. T. 1944, Bull. Astron. Inst. Netherlands, 10, 55 [NASA ADS] [Google Scholar]
  69. Pang, G., Shen, C., Cao, L., & van den Hengel, A. 2021, ACM Comput. Surv., 54 [Google Scholar]
  70. Parker, L., Lanusse, F., Golkar, S., et al. 2024, MNRAS, 531, 4990 [Google Scholar]
  71. Paszke, A., Gross, S., Massa, F., et al. 2019, in Advances in Neural Information Processing Systems (NIPS), 32 (Curran Associates, Inc.), 8024 [Google Scholar]
  72. Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, J. Mach. Learn. Res., 12, 2825 [Google Scholar]
  73. Prudil, Z., Dékány, I., Catelan, M., et al. 2019, MNRAS, 484, 4833 [NASA ADS] [Google Scholar]
  74. Radford, A., Kim, J. W., Hallacy, C., et al. 2021, in International Conference on Machine Learning, PMLR, 8748 [Google Scholar]
  75. Rimoldini, L., Eyer, L., Audard, M., et al. 2022, Gaia DR3 documentation Chapter 10: Variability, Gaia DR3 documentation, European Space Agency; Gaia Data Processing and Analysis Consortium. Online at https://gea.esac.esa.int/archive/documentation/GDR3/index.html, 10 [Google Scholar]
  76. Rimoldini, L., Holl, B., Gavras, P., et al. 2023, A&A, 674, A14 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  77. Rizhko, M., & Bloom, J. S. 2025, AJ, 170, 28 [Google Scholar]
  78. Ruz-Mieres, D. 2024 https://doi.org/10.5281/zenodo.11617977 [Google Scholar]
  79. Sánchez-Sáez, P., Lira, H., Martí, L., et al. 2021, AJ, 162, 206 [CrossRef] [Google Scholar]
  80. Sartoretti, P., Marchal, O., Babusiaux, C., et al. 2023, A&A, 674, A6 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  81. Scargle, J. D. 1982, ApJ, 263, 835 [Google Scholar]
  82. Skarka, M., Prudil, Z., & Jurcsik, J. 2020, MNRAS, 494, 1237 [NASA ADS] [CrossRef] [Google Scholar]
  83. Smith, H. A. 1995, Cambr. Astrophys. Ser., 27 [Google Scholar]
  84. Soraisam, M. D., Saha, A., Matheson, T., et al. 2020, ApJ, 892, 112 [Google Scholar]
  85. The Multimodal Universe Collaboration (Audenaert, J., et al.) 2024, in The Thirty-eight Conference on Neural Information Processing Systems (NIPS) Datasets and Benchmarks Track [arXiv:2412.02527] [Google Scholar]
  86. Udalski, A., Szymanski, M. K., Soszynski, I., & Poleski, R. 2008, Acta Astron., 58, 69 [NASA ADS] [Google Scholar]
  87. Zhang, G., Helfer, T., Gagliano, A. T., Mishra-Sharma, S., & Ashley Villar, V. 2024, Mach. Learn. Sci. Technol., 5, 045069 [Google Scholar]

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