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Home All issues Volume 701 (September 2025) A&A, 701 (2025) A96 References
Open Access
Issue
A&A
Volume 701, September 2025
Article Number A96
Number of page(s) 16
Section Catalogs and data
DOI https://doi.org/10.1051/0004-6361/202553739
Published online 04 September 2025
  1. Acquaviva, V. 2023, Machine Learning for Physics and Astronomy [Google Scholar]
  2. Agrawal, V. K., & Nandi, A. 2015, MNRAS, 446, 3926 [NASA ADS] [CrossRef] [Google Scholar]
  3. Alashwal, H., El Halaby, M., Crouse, J. J., Abdalla, A., & Moustafa, A. A. 2019, Front. Computat. Neurosci., 13 [Google Scholar]
  4. Amato, R., Gúrpide, A., Webb, N. A., Godet, O., & Middleton, M. J. 2023, A&A, 669, A130 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  5. Assent, I. 2012, WIREs Data Mining Knowledge Discov., 2, 340 [Google Scholar]
  6. Atapin, K., Fabrika, S., & Caballero-García, M. D. 2019, MNRAS, 486, 2766 [NASA ADS] [CrossRef] [Google Scholar]
  7. Atapin, K., Vinokurov, A., Sarkisyan, A., et al. 2024, MNRAS, 527, 10185 [Google Scholar]
  8. Bachetti, M. 2018, HENDRICS: High ENergy Data Reduction Interface from the Command Shell, Astrophysics Source Code Library [record ascl:1805.019] [Google Scholar]
  9. Bachetti, M., Harrison, F. A., Walton, D. J., et al. 2014, Nature, 514, 202 [NASA ADS] [CrossRef] [Google Scholar]
  10. Bachetti, M., Huppenkothen, D., Stevens, A., et al. 2024, J. Open Source Softw., 9, 7389 [Google Scholar]
  11. Barra, F., Pinto, C., Middleton, M., et al. 2024, A&A, 682, A94 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  12. Belfiore, A., Salvaterra, R., Sidoli, L., et al. 2024, ApJ, 965, 78 [NASA ADS] [CrossRef] [Google Scholar]
  13. Blanco-Justicia, A., Domingo-Ferrer, J., Martínez, S., & Sánchez, D. 2020, Knowledge-Based Syst., 194, 105532 [Google Scholar]
  14. Brightman, M., Harrison, F. A., Fürst, F., et al. 2018, Nat. Astron., 2, 312 [CrossRef] [Google Scholar]
  15. Brightman, M., Walton, D. J., Xu, Y., et al. 2020, ApJ, 889, 71 [Google Scholar]
  16. Carpano, S., Haberl, F., Maitra, C., & Vasilopoulos, G. 2018, MNRAS, 476, L45 [NASA ADS] [CrossRef] [Google Scholar]
  17. Colbert, E. J. M., & Mushotzky, R. F. 1999, ApJ, 519, 89 [NASA ADS] [CrossRef] [Google Scholar]
  18. Colbert, E. J. M., Heckman, T. M., Ptak, A. F., Strickland, D. K., & Weaver, K. A. 2004, ApJ, 602, 231 [NASA ADS] [CrossRef] [Google Scholar]
  19. de Mijolla, D., Frye, C., Kunesch, M., Mansir, J., & Feige, I. 2020, arXiv e-prints [arXiv:2010.07384] [Google Scholar]
  20. Deng, Y., Tang, F., Dong, W., et al. 2019, Multimedia Tools Appl., 78, 19305 [Google Scholar]
  21. Dewangan, G. C., Griffiths, R. E., Choudhury, M., Miyaji, T., & Schurch, N. J. 2005, ApJ, 635, 198 [NASA ADS] [CrossRef] [Google Scholar]
  22. Evans, I. N., Primini, F. A., Miller, J. B., et al. 2020a, in American Astronomical Society Meeting Abstracts, 235, 154.05 [NASA ADS] [Google Scholar]
  23. Evans, P. A., Page, K. L., Osborne, J. P., et al. 2020b, ApJS, 247, 54 [Google Scholar]
  24. Fabbiano, G. 1989, ARA&A, 27, 87 [NASA ADS] [CrossRef] [Google Scholar]
  25. Fabrika, S. N., Atapin, K. E., Vinokurov, A. S., & Sholukhova, O. N. 2021, Astrophys. Bull., 76, 6 [NASA ADS] [CrossRef] [Google Scholar]
  26. Fürst, F., Walton, D. J., Harrison, F. A., et al. 2016, ApJ, 831, L14 [Google Scholar]
  27. Fürst, F., Walton, D. J., Heida, M., et al. 2021, A&A, 651, A75 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  28. Fürst, F., Walton, D. J., Israel, G. L., et al. 2023, A&A, 672, A140 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  29. Gladstone, J. C., Roberts, T. P., & Done, C. 2009, MNRAS, 397, 1836 [NASA ADS] [CrossRef] [Google Scholar]
  30. Gúrpide, A., Godet, O., Koliopanos, F., Webb, N., & Olive, J. F. 2021, A&A, 649, A104 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  31. Heil, L. M., Vaughan, S., & Roberts, T. P. 2009, MNRAS, 397, 1061 [CrossRef] [Google Scholar]
  32. Hu, C.-P., Kong, A. K. H., Ng, C. Y., & Li, K. L. 2018, ApJ, 864, 64 [Google Scholar]
  33. Huppenkothen, D., Bachetti, M., Stevens, A. L., et al. 2019, ApJ, 881, 39 [Google Scholar]
  34. Imbrogno, M., Motta, S. E., Amato, R., et al. 2024, A&A, 689, A284 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  35. ISO 2017, Standards by ISO/IEC JTC 1/SC 42 – Artificial Intelligence [Google Scholar]
  36. Israel, G. L., Belfiore, A., Stella, L., et al. 2017a, Science, 355, 817 [NASA ADS] [CrossRef] [Google Scholar]
  37. Israel, G. L., Papitto, A., Esposito, P., et al. 2017b, MNRAS, 466, L48 [NASA ADS] [CrossRef] [Google Scholar]
  38. Jain, A. K., & Dubes, R. C. 1988, Algorithms for Clustering Data (Prentice-Hall, Inc.) [Google Scholar]
  39. Kaaret, P., Feng, H., & Roberts, T. P. 2017, ARA&A, 55, 303 [Google Scholar]
  40. Kalogera, V., & Baym, G. 1996, ApJ, 470, L61 [NASA ADS] [CrossRef] [Google Scholar]
  41. Kaur, A., Henze, M., Haberl, F., et al. 2012, A&A, 538, A49 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  42. Kennea, J. A., Lien, A. Y., Krimm, H. A., Cenko, S. B., & Siegel, M. H. 2017, Astronomer’s Telegram, 10809, 1 [Google Scholar]
  43. King, A. R., Davies, M. B., Ward, M. J., Fabbiano, G., & Elvis, M. 2001, ApJ, 552, L109 [NASA ADS] [CrossRef] [Google Scholar]
  44. King, A., Lasota, J.-P., & Middleton, M. 2023, New A Rev., 96, 101672 [NASA ADS] [CrossRef] [Google Scholar]
  45. Kingsford, C., & Salzberg, S. L. 2008, Nat. Biotechnol., 26, 1011 [Google Scholar]
  46. Kirch, W., ed. 2008, Encyclopedia of Public Health (Springer Netherlands), 1484 [Google Scholar]
  47. Kovlakas, K., Zezas, A., Andrews, J. J., et al. 2020, MNRAS, 498, 4790 [Google Scholar]
  48. Krone-Martins, A., & Moitinho, A. 2013, A&A, 561, A57 [Google Scholar]
  49. MacQueen, J., et al. 1967, Some methods for classification and analysis of multivariate observations [Google Scholar]
  50. Majumder, S., Das, S., Agrawal, V. K., & Nandi, A. 2023, MNRAS, 526, 2086 [Google Scholar]
  51. Makarov, D., Prugniel, P., Terekhova, N., Courtois, H., & Vauglin, I. 2014, A&A, 570, A13 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  52. Massey, F. J. 1951, J. Am. Statist. Assoc., 46, 68 [CrossRef] [Google Scholar]
  53. Michaud, P. 1997, Future Gener. Comput. Syst., 13, 135 [Google Scholar]
  54. Middleton, M. J., Sutton, A. D., Roberts, T. P., Jackson, F. E., & Done, C. 2012, MNRAS, 420, 2969 [NASA ADS] [CrossRef] [Google Scholar]
  55. Mineo, S., Rappaport, S., Steinhorn, B., et al. 2013, ApJ, 771, 133 [Google Scholar]
  56. Pasham, D. R., Cenko, S. B., Zoghbi, A., et al. 2015, ApJ, 811, L11 [NASA ADS] [CrossRef] [Google Scholar]
  57. Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, J. Mach. Learn. Res., 12, 2825 [Google Scholar]
  58. Pinciroli Vago, N. O., & Fraternali, P. 2023, Neural Comput. Appl., 35, 19253 [Google Scholar]
  59. Pinto, C., & Walton, D. J. 2023, arXiv e-prints, [arXiv:2302.00006] [Google Scholar]
  60. Pinto, C., Walton, D. J., Kara, E., et al. 2020, MNRAS, 492, 4646 [Google Scholar]
  61. Pinto, C., Soria, R., Walton, D. J., et al. 2021, MNRAS, 505, 5058 [NASA ADS] [CrossRef] [Google Scholar]
  62. Pintore, F., Zampieri, L., Stella, L., et al. 2017, ApJ, 836, 113 [Google Scholar]
  63. Pintore, F., Zampieri, L., Mereghetti, S., et al. 2018, MNRAS, 479, 4271 [Google Scholar]
  64. Pintore, F., Pinto, C., Rodriguez-Castillo, G., et al. 2025, A&A, 695, A238 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  65. Poutanen, J., Lipunova, G., Fabrika, S., Butkevich, A. G., & Abolmasov, P. 2007, MNRAS, 377, 1187 [NASA ADS] [CrossRef] [Google Scholar]
  66. Pérez-Díaz, V. S., Martínez-Galarza, J. R., Caicedo, A., & D’Abrusco, R. 2024, MNRAS, 528, 4852 [CrossRef] [Google Scholar]
  67. Quintin, E., Webb, N. A., Gúrpide, A., Bachetti, M., & Fürst, F. 2021, MNRAS, 503, 5485 [NASA ADS] [CrossRef] [Google Scholar]
  68. Ransom, S. M., Eikenberry, S. S., & Middleditch, J. 2002, AJ, 124, 1788 [Google Scholar]
  69. Rao, F., Feng, H., & Kaaret, P. 2010, ApJ, 722, 620 [Google Scholar]
  70. Reynolds, D. 2009, Gaussian Mixture Models (Springer US), 659 [Google Scholar]
  71. Reynolds, D. A., et al. 2009, Encyclop. Biometrics, 741 [Google Scholar]
  72. Rodríguez Castillo, G. A., Israel, G. L., Belfiore, A., et al. 2020, ApJ, 895, 60 [Google Scholar]
  73. Rousseeuw, P. J. 1987, J. Computat. Appl. Math., 20, 53 [CrossRef] [Google Scholar]
  74. Sacchi, A., Imbrogno, M., Motta, S. E., et al. 2024, A&A, 689, A217 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  75. Sathyaprakash, R., Roberts, T. P., Walton, D. J., et al. 2019, MNRAS, 488, L35 [NASA ADS] [CrossRef] [Google Scholar]
  76. Sathyaprakash, R., Roberts, T. P., Grisé, F., et al. 2022, MNRAS, 511, 5346 [NASA ADS] [CrossRef] [Google Scholar]
  77. Sen, S., Agarwal, S., Chakraborty, P., & Singh, K. P. 2022, Exp. Astron., 53, 1 [NASA ADS] [CrossRef] [Google Scholar]
  78. Slijepcevic, I. V., Scaife, A. M. M., Walmsley, M., et al. 2022, MNRAS, 514, 2599 [NASA ADS] [CrossRef] [Google Scholar]
  79. Stoitsas, K., Bahulikar, S., de Munter, L., et al. 2022, Sci. Rep., 12, 16990 [Google Scholar]
  80. Strohmayer, T. E., & Mushotzky, R. F. 2009, ApJ, 703, 1386 [NASA ADS] [CrossRef] [Google Scholar]
  81. Strohmayer, T. E., & Markwardt, C. B. 2011, Astronomer’s Telegram, 3121, 1 [Google Scholar]
  82. Strohmayer, T. E., Mushotzky, R. F., Winter, L., et al. 2007, ApJ, 660, 580 [NASA ADS] [CrossRef] [Google Scholar]
  83. Strüder, L., Briel, U., Dennerl, K., et al. 2001, A&A, 365, L18 [Google Scholar]
  84. Sutton, A. D., Roberts, T. P., & Middleton, M. J. 2013, MNRAS, 435, 1758 [NASA ADS] [CrossRef] [Google Scholar]
  85. Townsend, L. J., Kennea, J. A., Coe, M. J., et al. 2017, MNRAS, 471, 3878 [NASA ADS] [CrossRef] [Google Scholar]
  86. Tranin, H., Webb, N., Godet, O., & Quintin, E. 2024, A&A, 681, A16 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  87. Tsygankov, S. S., Doroshenko, V., Lutovinov, A. A., Mushtukov, A. A., & Poutanen, J. 2017, A&A, 605, A39 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  88. Vasilopoulos, G., Ray, P. S., Gendreau, K. C., et al. 2020, MNRAS, 494, 5350 [NASA ADS] [CrossRef] [Google Scholar]
  89. Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nat. Methods, 17, 261 [Google Scholar]
  90. Walton, D. J., Fürst, F., Heida, M., et al. 2018, ApJ, 856, 128 [NASA ADS] [CrossRef] [Google Scholar]
  91. Walton, D. J., Pinto, C., Nowak, M., et al. 2020, MNRAS, 494, 6012 [Google Scholar]
  92. Walton, D. J., Mackenzie, A. D. A., Gully, H., et al. 2022, MNRAS, 509, 1587 [Google Scholar]
  93. Wang, L., Zhang, X., Su, H., & Zhu, J. 2024, IEEE Trans. Pattern Anal. Mach. Intell., 46, 5362 [Google Scholar]
  94. Webb, N. A., Coriat, M., Traulsen, I., et al. 2020, A&A, 641, A136 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  95. Weng, S.-S., Ge, M.-Y., Zhao, H.-H., et al. 2017, ApJ, 843, 69 [Google Scholar]
  96. Wiktorowicz, G., Sobolewska, M., Lasota, J.-P., & Belczynski, K. 2017, ApJ, 846, 17 [NASA ADS] [CrossRef] [Google Scholar]
  97. Wilson-Hodge, C. A., Malacaria, C., Jenke, P. A., et al. 2018, ApJ, 863, 9 [Google Scholar]
  98. Zampieri, L., & Roberts, T. P. 2009, MNRAS, 400, 677 [NASA ADS] [CrossRef] [Google Scholar]

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