Open Access
Issue
A&A
Volume 689, September 2024
Article Number A289
Number of page(s) 12
Section Numerical methods and codes
DOI https://doi.org/10.1051/0004-6361/202449475
Published online 19 September 2024
  1. Allam, T., & McEwen, J. D. 2024, RASTI, 3, 209 [Google Scholar]
  2. Astorga, N., Huijse, P., Estévez, P. A., & Förster, F. 2018, in 2018 International Joint Conference on Neural Networks (IJCNN), 1 [Google Scholar]
  3. Astropy Collaboration (Price-Whelan, A. M., et al.) 2022, ApJ, 935, 167 [NASA ADS] [CrossRef] [Google Scholar]
  4. Becker, I., Pichara, K., Catelan, M., et al. 2020, MNRAS, 493, 2981 [NASA ADS] [CrossRef] [Google Scholar]
  5. Bellm, E. C., Kulkarni, S. R., Graham, M. J., et al. 2018, PASP, 131, 018002 [Google Scholar]
  6. Boone, K. 2019, AJ, 158, 257 [NASA ADS] [CrossRef] [Google Scholar]
  7. Borne, K. D. 2010, Earth Sci. Informatics, 3, 5 [Google Scholar]
  8. Cabrera-Vives, G., Reyes, I., Förster, F., Estévez, P. A., & Maureira, J.-C. 2016, in 2016 International Joint Conference on Neural Networks (IJCNN) (IEEE), 251 [CrossRef] [Google Scholar]
  9. Cabrera-Vives, G., Reyes, I., Förster, F., Estévez, P. A., & Maureira, J.-C. 2017, ApJ, 836, 97 [NASA ADS] [CrossRef] [Google Scholar]
  10. Cabrera-Vives, G., Li, Z., Rainer, A., et al. 2022, in International Conference on Product-Focused Software Process Improvement (Springer), 21 [Google Scholar]
  11. Carrasco-Davis, R., Cabrera-Vives, G., Förster, F., et al. 2019, PASP, 131, 108006 [NASA ADS] [CrossRef] [Google Scholar]
  12. Carrasco-Davis, R., Reyes, E., Valenzuela, C., et al. 2021, AJ, 162, 231 [NASA ADS] [CrossRef] [Google Scholar]
  13. Charnock, T., & Moss, A. 2017, ApJ, 837, L28 [NASA ADS] [CrossRef] [Google Scholar]
  14. Chen, C., Liaw, A., Breiman, L., et al. 2004, Univ. Calif. Berkeley, 110, 24 [Google Scholar]
  15. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. 2019, arXiv e-prints [arXiv: 1810.04805] [Google Scholar]
  16. Dieleman, S., Willett, K. W., & Dambre, J. 2015, MNRAS, 450, 1441 [NASA ADS] [CrossRef] [Google Scholar]
  17. Donoso-Oliva, C., Cabrera-Vives, G., Protopapas, P., Carrasco-Davis, R., & Estévez, P. A. 2021, MNRAS, 505, 6069 [CrossRef] [Google Scholar]
  18. Donoso-Oliva, C., Becker, I., Protopapas, P., et al. 2023, A&A, 670, A54 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  19. Dosovitskiy, A., Beyer, L., Kolesnikov, A., et al. 2021, ICLR [Google Scholar]
  20. Förster, F., Moriya, T., Maureira, J., et al. 2018, Nat. Astron., 2, 808 [CrossRef] [Google Scholar]
  21. Förster, F., Muñoz Arancibia, A. M., Reyes-Jainaga, I., et al. 2022, AJ, 164, 195 [CrossRef] [Google Scholar]
  22. Fraga, B. M. O., Bom, C. R., Santos, A., et al. 2024, A&A submitted [arXiv:2404.08798] [Google Scholar]
  23. Förster, F., Cabrera-Vives, G., Castillo-Navarrete, E., et al. 2021, AJ, 161, 242 [CrossRef] [Google Scholar]
  24. Gagliano, A., Contardo, G., Foreman-Mackey, D., Malz, A. I., & Aleo, P. D. 2023, ApJ, 954, 6 [Google Scholar]
  25. Gómez, C., Neira, M., Hernández Hoyos, M., Arbeláez, P., & Forero-Romero, J. E. 2020, MNRAS, 499, 3130 [CrossRef] [Google Scholar]
  26. Gorishniy, Y., Rubachev, I., Khrulkov, V., & Babenko, A. 2021, in Advances in Neural Information Processing Systems, 34, eds. M. Ranzato, A. Beygelz-imer, Y. Dauphin, P. Liang, & J. W. Vaughan (Curran Associates, Inc.), 18932 [Google Scholar]
  27. Graham, M. J., Kulkarni, S., Bellm, E. C., et al. 2019, PASP, 131, 078001 [NASA ADS] [CrossRef] [Google Scholar]
  28. Hendrycks, D., & Gimpel, K. 2016, arXiv e-prints [arXiv: 1606.08415] [Google Scholar]
  29. Hložek, R., Malz, A., Ponder, K., et al. 2023, ApJS, 267, 25 [Google Scholar]
  30. Huijse, P., Estevez, P. A., Protopapas, P., Principe, J. C., & Zegers, P. 2014, IEEE Computat. Intell. Mag., 9, 27 [CrossRef] [Google Scholar]
  31. Ishida, E., Mondon, F., Sreejith, S., et al. 2021, A&A, 650, A195 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  32. Ivezic, Ž., Kahn, S. M., Tyson, J. A., et al. 2019, ApJ, 873, 111 [NASA ADS] [CrossRef] [Google Scholar]
  33. Jamal, S., & Bloom, J. S. 2020, ApJS, 250, 30 [NASA ADS] [CrossRef] [Google Scholar]
  34. Kingma, D., & Ba, J. 2015, in International Conference on Learning Representations (ICLR), San Diega, CA, USA [Google Scholar]
  35. Komatsu, E., Dunkley, J., Nolta, M., et al. 2009, ApJS, 180, 330 [NASA ADS] [CrossRef] [Google Scholar]
  36. Mackenzie, C., Pichara, K., & Protopapas, P. 2016, ApJ, 820 [Google Scholar]
  37. Matheson, T., Stubens, C., Wolf, N., et al. 2021, AJ, 161, 107 [NASA ADS] [CrossRef] [Google Scholar]
  38. Möller, A., Peloton, J., Ishida, E. E. O., et al. 2021, MNRAS, 501, 3272 [CrossRef] [Google Scholar]
  39. Moreno-Cartagena, D. A., Cabrera-Vives, G., Protopapas, P., et al. 2023, in Machine Learning for Astrophysics. Workshop at the Fortieth International Conference on Machine Learning (ICML 2023), 23 [Google Scholar]
  40. Muthukrishna, D., Narayan, G., Mandel, K. S., Biswas, R., & Hložek, R. 2019, PASP, 131, 118002 [NASA ADS] [CrossRef] [Google Scholar]
  41. Möller, A., & de Boissière, T. 2019, MNRAS, 491, 4277 [Google Scholar]
  42. Narayan, G., Zaidi, T., Soraisam, M. D., et al. 2018, ApJS, 236, 9 [NASA ADS] [CrossRef] [Google Scholar]
  43. Naul, B., Bloom, J., Perez, F., & van der Walt, S. 2018, Nat. Astron., 2, 151 [NASA ADS] [CrossRef] [Google Scholar]
  44. Neira, M., Gómez, C., Suárez-Pérez, J. F., et al. 2020, ApJS, 250, 11 [NASA ADS] [CrossRef] [Google Scholar]
  45. Nordin, J., Brinnel, V., van Santen, J., et al. 2019, A&A, 631, A147 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  46. Nun, I., Protopapas, P., Sim, B., & Chen, W. 2016, AJ, 152, 71 [NASA ADS] [CrossRef] [Google Scholar]
  47. Nun, I., Protopapas, P., Sim, B., et al. 2017, Astrophysics Source Code Library, [record ascl:1711.017] [Google Scholar]
  48. O’Donnell, J. E. 1994, ApJ, 422, 158 [Google Scholar]
  49. Pérez-Carrasco, M., Cabrera-Vives, G., Hernández-Garcia, L., et al. 2023, in Machine Learning for Astrophysics. Workshop at the Fortieth International Conference on Machine Learning (ICML 2023), 23 [Google Scholar]
  50. Perez-Carrasco, M., Cabrera-Vives, G., Hernandez-García, L., et al. 2023, AJ, 166, 151 [NASA ADS] [CrossRef] [Google Scholar]
  51. Pimentel, Ó., Estévez, P. A., & Forster, F. 2023, AJ, 165, 18 [NASA ADS] [CrossRef] [Google Scholar]
  52. Pruzhinskaya, M. V., Malanchev, K. L., Kornilov, M. V., et al. 2019, MNRAS, 489, 3591 [Google Scholar]
  53. Rodriguez-Mancini, D., Li, Z., Valenzuela, C., Cabrera-Vives, G., & Förster, F. 2022, IEEE Softw., 39, 28 [CrossRef] [Google Scholar]
  54. Russeil, E., Ishida, E. E. O., Le Montagner, R., Peloton, J., & Moller, A. 2022, [arXiv:2211.10987] [Google Scholar]
  55. Sánchez, A., Cabrera, G., Huijse, P., & Förster, F. 2022, in Machine Learning and the Physical Sciences Workshop, 35th Conference on Neural Information Processing Systems (NeurIPS) [Google Scholar]
  56. Sánchez-Sáez, P., Lira, H., Martí, L., et al. 2021a, AJ, 162, 206 [CrossRef] [Google Scholar]
  57. Sánchez-Sáez, P., Reyes, I., Valenzuela, C., et al. 2021b, AJ, 161, 141 [CrossRef] [Google Scholar]
  58. Sánchez-Sáez, P., Arredondo, J., Bayo, A., et al. 2023, A&A, 675, A195 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  59. Smith, K. W., Williams, R. D., Young, D. R., et al. 2019, RNAAS, 3, 26 [NASA ADS] [Google Scholar]
  60. Vaswani, A., Shazeer, N., Parmar, N., et al. 2017, in Advances in Neural Information Processing Systems, 30, eds. I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Curran Associates, Inc.) [Google Scholar]
  61. Villar, V. A. 2022, arXiv e-prints [arXiv:2211.04480] [Google Scholar]

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