EDP Sciences logo
Subscriber Authentication Point
Search
Menu
Home All issues Volume 689 (September 2024) A&A, 689 (2024) A282 References
Open Access
Issue
A&A
Volume 689, September 2024
Article Number A282
Number of page(s) 10
Section Galactic structure, stellar clusters and populations
DOI https://doi.org/10.1051/0004-6361/202449791
Published online 19 September 2024
  1. Agarwal, M., Rao, K. K., Vaidya, K., & Bhattacharya, S. 2021, MNRAS, 502, 2582 [CrossRef] [Google Scholar]
  2. Ascenso, J., Alves, J., & Lago, M. T. V. T. 2009, A&A, 495, 147 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  3. Astropy Collaboration (Robitaille, T. P., et al.) 2013, A&A, 558, A33 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  4. Bação, F., Lobo, V., & Painho, M. 2005, in Computational Science-ICCS 2005: 5th International Conference, Atlanta, GA, USA, May 22–25, 2005, Proceedings, Part III 5, Springer, 476 [Google Scholar]
  5. Bebis, G., & Georgiopoulos, M. 1994, IEEE Potentials, 13, 27 [CrossRef] [Google Scholar]
  6. Bishop, C. M. 2006, Pattern Recognition and Machine Learning (Springer) [Google Scholar]
  7. Bland-Hawthorn, J., & Gerhard, O. 2016, ARA&A, 54, 529 [Google Scholar]
  8. Bouma, L. G., Curtis, J. L., Hartman, J. D., Winn, J. N., & Bakos, G. Á. 2021, AJ, 162, 197 [NASA ADS] [CrossRef] [Google Scholar]
  9. Breiman, L. 2001, Mach. Learn., 45, 5 [Google Scholar]
  10. Breiman, L., Friedman, J., Olshen, R., & Stone, C. 1984, Classification and Regression Trees (Chapman and Hall/CRC) [Google Scholar]
  11. Campello, R. J. G. B., Moulavi, D., & Sander, J. 2013, in Advances in Knowledge Discovery and Data Mining, eds. J. Pei, V. S. Tseng, L. Cao, H. Motoda, & G. Xu (Berlin, Heidelberg: Springer Berlin Heidelberg), 160 [Google Scholar]
  12. Cantat-Gaudin, T., Jordi, C., Vallenari, A., et al. 2018, A&A, 618, A93 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  13. Cantat-Gaudin, T., Anders, F., Castro-Ginard, A., et al. 2020, A&A, 640, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  14. Castro-Ginard, A., Jordi, C., Luri, X., et al. 2020, A&A, 635, A45 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  15. Chen, L., Hou, J. L., & Wang, J. J. 2003, AJ, 125, 1397 [NASA ADS] [CrossRef] [Google Scholar]
  16. Chen, Y., Girardi, L., Fu, X., et al. 2019, A&A, 632, A105 [EDP Sciences] [Google Scholar]
  17. Conrad, C., Scholz, R. D., Kharchenko, N. V., et al. 2017, A&A, 600, A106 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  18. Cortes, C., & Vapnik, V. 1995, Mach. Learn., 20, 273 [Google Scholar]
  19. Cover, T. M., & Hart, P. E. 1967, Inform. Theory IEEE Trans., 13, 21 [CrossRef] [Google Scholar]
  20. Das, S. R., Gupta, S., Prakash, P., Samal, M., & Jose, J. 2023, ApJ, 948, 7 [NASA ADS] [CrossRef] [Google Scholar]
  21. Deb, S., Baruah, A., & Kumar, S. 2022, MNRAS, 515, 4685 [NASA ADS] [CrossRef] [Google Scholar]
  22. de la Fuente Marcos, R., & de la Fuente Marcos, C. 2004, New A, 9, 475 [NASA ADS] [CrossRef] [Google Scholar]
  23. Dias, W. S., Monteiro, H., Caetano, T. C., et al. 2014, A&A, 564, A79 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  24. Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. 1996, in Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD) (ACM), 226 [Google Scholar]
  25. Fürnkranz, J. 2010, Decision Tree, eds. C. Sammut, & G. I. Webb (Boston, MA: Springer US), 263 [Google Scholar]
  26. Gaia Collaboration (Brown, A. G. A., et al.) 2016a, A&A, 595, A2 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  27. Gaia Collaboration (Prusti, T., et al.) 2016b, A&A, 595, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  28. Gaia Collaboration (Brown, A. G. A., et al.) 2018, A&A, 616, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  29. Gaia Collaboration (Brown, A. G. A., et al.) 2021, A&A, 649, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  30. Gaia Collaboration (Vallenari, A., et al.) 2023, A&A, 674, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  31. Gao, X.-H. 2014, Res. Astron. Astrophys., 14, 159 [CrossRef] [Google Scholar]
  32. Gao, X. 2018a, ApJ, 869, 9 [NASA ADS] [CrossRef] [Google Scholar]
  33. Gao, X.-H. 2018b, Ap&SS, 363, 232 [NASA ADS] [CrossRef] [Google Scholar]
  34. Gao, X.-h. 2019a, PASP, 131, 044101 [NASA ADS] [CrossRef] [Google Scholar]
  35. Gao, X.-h. 2019b, MNRAS, 486, 5405 [NASA ADS] [CrossRef] [Google Scholar]
  36. Ghosh, E. M., Sulistiyowati, Tucio, P., & Fajrin, M. 2022, J. Phys. Conf. Ser., 2214, 012009 [NASA ADS] [CrossRef] [Google Scholar]
  37. Goutte, C., & Gaussier, E. 2005, in Lecture Notes in Computer Science, 3408, 345 [Google Scholar]
  38. Guido, R. M. D., Tucio, P. B., Kalaw, J. B., & Geraldo, L. E. 2023, in IOP Conference Series: Earth and Environmental Science, 1167, 012010 [NASA ADS] [CrossRef] [Google Scholar]
  39. Hunt, E. L., & Reffert, S. 2021, A&A, 646, A104 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  40. Hunt, E. L., & Reffert, S. 2023, A&A, 673, A114 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  41. Jadhav, V. V., Pennock, C. M., Subramaniam, A., Sagar, R., & Nayak, P. K. 2021, MNRAS, 503, 236 [NASA ADS] [CrossRef] [Google Scholar]
  42. Jaehnig, K., Bird, J., & Holley-Bockelmann, K. 2021, ApJ, 923, 129 [NASA ADS] [CrossRef] [Google Scholar]
  43. Just, A., Berczik, P., Petrov, M. I., & Ernst, A. 2009, MNRAS, 392, 969 [Google Scholar]
  44. Kalambay, M. T., Naurzbayeva, A. Z., Otebay, A. B., et al. 2022, Recent Contrib. Phys., 83, 4 [Google Scholar]
  45. Kharchenko, N. V., Piskunov, A. E., Röser, S., Schilbach, E., & Scholz, R. D. 2005, A&A, 438, 1163 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  46. Kharchenko, N. V., Piskunov, A. E., Schilbach, E., Röser, S., & Scholz, R. D. 2012, A&A, 543, A156 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  47. Kohl, M. 2012, Int. J. Statist. Med. Res., 1, 79 [CrossRef] [Google Scholar]
  48. Kounkel, M., & Covey, K. 2019, AJ, 158, 122 [Google Scholar]
  49. Krone-Martins, A., & Moitinho, A. 2015, ascl:1504.001 [Google Scholar]
  50. Krumholz, M. R., McKee, C. F., & Bland-Hawthorn, J. 2019, ARA&A, 57, 227 [NASA ADS] [CrossRef] [Google Scholar]
  51. Lada, C. J., & Lada, E. A. 2003, ARA&A, 41, 57 [Google Scholar]
  52. Liu, L., & Pang, X. 2019, ApJS, 245, 32 [NASA ADS] [CrossRef] [Google Scholar]
  53. Lodieu, N., Pérez-Garrido, A., Smart, R. L., & Silvotti, R. 2019, A&A, 628, A66 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  54. Mahmudunnobe, M., Hasan, P., Raja, M., & Hasan, S. N. 2021, Eur. Phys. J. Special Top., 230, 2177 [NASA ADS] [CrossRef] [Google Scholar]
  55. Maíz Apellániz, J., & Weiler, M. 2018, A&A, 619, A180 [Google Scholar]
  56. Meingast, S., & Alves, J. 2019, A&A, 621, A3 [Google Scholar]
  57. Meingast, S., Alves, J., & Rottensteiner, A. 2021, A&A, 645, A84 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  58. Noormohammadi, M., Khakian Ghomi, M., & Haghi, H. 2023, MNRAS, 523, 3538 [NASA ADS] [CrossRef] [Google Scholar]
  59. Olivares, J., Lodieu, N., Béjar, V. J. S., et al. 2023, A&A, 675, A28 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  60. Pang, X., Li, Y., Tang, S.-Y., Pasquato, M., & Kouwenhoven, M. B. N. 2020, ApJ, 900, L4 [NASA ADS] [CrossRef] [Google Scholar]
  61. Pang, X., Tang, S.-Y., Li, Y., et al. 2022, ApJ, 931, 156 [NASA ADS] [CrossRef] [Google Scholar]
  62. Pang, X., Liao, S., Li, J., et al. 2024, ApJ, 966, 169 [Google Scholar]
  63. Parmentier, G., & Pfalzner, S. 2013, in Protostars and Planets VI Posters [Google Scholar]
  64. Paszke, A., Gross, S., Massa, F., et al. 2019, in Advances in Neural Information Processing Systems 32, eds. H. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché-Buc, E. Fox, & R. Garnett (Curran Associates, Inc.), 8024 [Google Scholar]
  65. Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, J. Mach. Learn. Res., 12, 2825 [Google Scholar]
  66. Pera, M. S., Perren, G. I., Moitinho, A., Navone, H. D., & Vazquez, R. A. 2021, A&A, 650, A109 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  67. Peterson, L. E. 2009, Scholarpedia, 4, 1883 [CrossRef] [Google Scholar]
  68. Phelps, R. L., & Janes, K. A. 1994, ApJS, 90, 31 [NASA ADS] [CrossRef] [Google Scholar]
  69. Portegies Zwart, S. F., McMillan, S. L. W., & Gieles, M. 2010, ARA&A, 48, 431 [NASA ADS] [CrossRef] [Google Scholar]
  70. Renaud, F. 2018, New A Rev., 81, 1 [CrossRef] [Google Scholar]
  71. Röser, S., & Schilbach, E. 2019, A&A, 627, A4 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  72. Röser, S., Schilbach, E., & Goldman, B. 2019, A&A, 621, A2 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  73. Ruprecht, J., Balázs, B., & White, R. E. 1981, Akad. Kiado, 0 [Google Scholar]
  74. Rybizki, J. 2019, Galaxia_wrap: Galaxia wrapper for generating mock stellar surveys, Astrophysics Source Code Library, record ascl:1901.005 [Google Scholar]
  75. Sharma, S., Bland-Hawthorn, J., Johnston, K. V., & Binney, J. 2011, ApJ, 730, 3 [NASA ADS] [CrossRef] [Google Scholar]
  76. Shukirgaliyev, B., Parmentier, G., Berczik, P., & Just, A. 2017, A&A, 605, A119 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  77. Shukirgaliyev, B., Otebay, A., Just, A., et al. 2019, Reports of NAS RK. Physicomathematical series, 130 [Google Scholar]
  78. Shukirgaliyev, B., Otebay, A., Sobolenko, M., et al. 2021, A&A, 654, A53 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  79. Sindhu Meena, K., & Suriya, S. 2020, in Proceedings of International Conference on Artificial Intelligence, Smart Grid and Smart City Applications, eds. L. A. Kumar, L. S. Jayashree, & R. Manimegalai (Cham: Springer International Publishing), 627 [CrossRef] [Google Scholar]
  80. Stock, J. 1956, ApJ, 123, 258 [NASA ADS] [CrossRef] [Google Scholar]
  81. Tang, S.-Y., Pang, X., Yuan, Z., et al. 2019, ApJ, 877, 12 [Google Scholar]
  82. Tucio, P. B., Guido, R. M. D., & Kalaw, J. B. 2023, IOP Conf. Ser.: Earth Environ. Sci., 1167, 012002 [NASA ADS] [CrossRef] [Google Scholar]
  83. van Groeningen, M. G. J., Castro-Ginard, A., Brown, A. G. A., Casamiquela, L., & Jordi, C. 2023, A&A, 675, A68 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  84. Yuan, Z., Chang, J., Banerjee, P., et al. 2018, ApJ, 863, 26 [NASA ADS] [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.