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Home All issues Volume 688 (August 2024) A&A, 688 (2024) A165 References
Open Access
Issue
A&A
Volume 688, August 2024
Article Number A165
Number of page(s) 15
Section Galactic structure, stellar clusters and populations
DOI https://doi.org/10.1051/0004-6361/202449938
Published online 15 August 2024
  1. Abdurro’uf, Accetta, K., Aerts, C., et al. 2022, ApJS, 259, 35 [NASA ADS] [CrossRef] [Google Scholar]
  2. Ankerst, M., Breunig, M. M., Kriegel, H.-P., & Sander, J. 1999, Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data, SIGMOD’99 (New York: Association for Computing Machinery) [Google Scholar]
  3. Bellman, R. 1958, Inf. Control, 1, 228 [CrossRef] [Google Scholar]
  4. Bird, J. C., Kazantzidis, S., & Weinberg, D. H. 2012, MNRAS, 420, 913 [NASA ADS] [CrossRef] [Google Scholar]
  5. Blanco-Cuaresma, S., Soubiran, C., Heiter, U., et al. 2015, A&A, 577, A47 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  6. Bland-Hawthorn, J., Krumholz, M. R., & Freeman, K. 2010, ApJ, 713, 166 [CrossRef] [Google Scholar]
  7. Bovy, J. 2016, ApJ, 817, 49 [NASA ADS] [CrossRef] [Google Scholar]
  8. Breiman, L. 2001, Mach. Learn., 45, 5 [Google Scholar]
  9. Bressan, A., Marigo, P., Girardi, L., et al. 2012, MNRAS, 427, 127 [NASA ADS] [CrossRef] [Google Scholar]
  10. Campello, R. J. G. B., Moulavi, D., & Sander, J. 2013, Advances in Knowledge Discovery and Data Mining (Berlin, Heidelberg: Springer-Verlag), 160 [Google Scholar]
  11. Cantat-Gaudin, T., Jordi, C., Vallenari, A., et al. 2018, A&A, 618, A93 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  12. Caruana, R. 1997, Mach. Learn., 28, 41 [CrossRef] [Google Scholar]
  13. Casamiquela, L., Castro-Ginard, A., Anders, F., & Soubiran, C. 2021, A&A, 654, A151 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  14. Chandola, V., Banerjee, A., & Kumar, V. 2009, ACM Comput. Surv., 41, 1 [CrossRef] [Google Scholar]
  15. Chen, T., & Guestrin, C. 2016, arXiv e-prints [arXiv:1603.02754] [Google Scholar]
  16. Cortes, C., & Vapnik, V. 1995, Mach. Learn., 20, 273 [Google Scholar]
  17. Cover, T., & Hart, P. 1967, IEEE Trans. Inf. Theory, 13, 21 [CrossRef] [Google Scholar]
  18. Crawshaw, M. 2020, arXiv e-prints [arXiv:2009.09796] [Google Scholar]
  19. Cui, X.-Q., Zhao, Y.-H., Chu, Y.-Q., et al. 2012, RAA, 12, 1197 [NASA ADS] [Google Scholar]
  20. De Silva, G. M., Freeman, K. C., Bland-Hawthorn, J., et al. 2015, MNRAS, 449, 2604 [NASA ADS] [CrossRef] [Google Scholar]
  21. Dias, W. S., Monteiro, H., Moitinho, A., et al. 2021, MNRAS, 504, 356 [NASA ADS] [CrossRef] [Google Scholar]
  22. Donor, J., Frinchaboy, P. M., Cunha, K., et al. 2018, AJ, 156, 142 [NASA ADS] [CrossRef] [Google Scholar]
  23. Donor, J., Frinchaboy, P. M., Cunha, K., et al. 2020, AJ, 159, 199 [NASA ADS] [CrossRef] [Google Scholar]
  24. Edvardsson, B., Andersen, J., Gustafsson, B., et al. 1993, A&A, 275, 101 [NASA ADS] [Google Scholar]
  25. Espinoza-Rojas, F., Chanamé, J., Jofré, P., & Casamiquela, L. 2021, ApJ, 920, 94 [NASA ADS] [CrossRef] [Google Scholar]
  26. Feng, Y., & Krumholz, M. R. 2014, Nature, 513, 523 [NASA ADS] [CrossRef] [Google Scholar]
  27. Freeman, K., & Bland-Hawthorn, J. 2002, ARA&A, 40, 487 [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 (Vallenari, A., et al.) 2023, A&A, 674, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  30. García Pérez, A. E., Allende Prieto, C., Holtzman, J. A., et al. 2016, AJ, 151, 144 [Google Scholar]
  31. Hastie, T., Tibshirani, R., & Friedman, J. 2009, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Series in Statistics (Springer) [Google Scholar]
  32. Hawkins, K. 2020, Am. Astron. Soc. Meet. Abstr., 235, 214.07 [NASA ADS] [Google Scholar]
  33. Ho, T. K. 1995, Proceedings of 3rd International Conference on Document Analysis and Recognition (IEEE), 278 [Google Scholar]
  34. Hunt, E. L., & Reffert, S. 2023, A&A, 673, A114 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  35. Jackson, H., Jofré, P., Yaxley, K., et al. 2021, MNRAS, 502, 32 [NASA ADS] [CrossRef] [Google Scholar]
  36. Jofré, P. 2021, ApJ, 920, 23 [CrossRef] [Google Scholar]
  37. Jofré, P., Heiter, U., & Soubiran, C. 2019, ARA&A, 57, 571 [Google Scholar]
  38. Johnson, J. W., Weinberg, D. H., Vincenzo, F., et al. 2021, MNRAS, 508, 4484 [NASA ADS] [CrossRef] [Google Scholar]
  39. Jönsson, H., Holtzman, J. A., Allende Prieto, C., et al. 2020, AJ, 160, 120 [Google Scholar]
  40. Kobayashi, C., Karakas, A. I., & Lugaro, M. 2020, ApJ, 900, 179 [Google Scholar]
  41. Krawczyk, B. 2016, Progr. Artif. Intell., 5, 221 [CrossRef] [Google Scholar]
  42. Krumholz, M. R., McKee, C. F., & Bland-Hawthorn, J. 2019, ARA&A, 57, 227 [NASA ADS] [CrossRef] [Google Scholar]
  43. Lada, C. J., & Lada, E. A. 2003, ARA&A, 41, 57 [Google Scholar]
  44. Leung, H. W., & Bovy, J. 2019, MNRAS, 483, 3255 [NASA ADS] [Google Scholar]
  45. Magrini, L., Viscasillas Vázquez, C., Spina, L., et al. 2023, A&A, 669, A119 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  46. Majewski, S. R., Schiavon, R. P., Frinchaboy, P. M., et al. 2017, AJ, 154, 94 [NASA ADS] [CrossRef] [Google Scholar]
  47. Manea, C., Hawkins, K., Ness, M. K., et al. 2023, AAS J., submitted [arXiv:2310.15257] [Google Scholar]
  48. Masseron, T., & Gilmore, G. 2015, MNRAS, 453, 1855 [CrossRef] [Google Scholar]
  49. Mitschang, A. W., De Silva, G., Zucker, D. B., et al. 2014, MNRAS, 438, 2753 [NASA ADS] [CrossRef] [Google Scholar]
  50. Myers, N., Donor, J., Spoo, T., et al. 2022, AJ, 164, 85 [NASA ADS] [CrossRef] [Google Scholar]
  51. Ness, M., Rix, H. W., Hogg, D. W., et al. 2018, ApJ, 853, 198 [NASA ADS] [CrossRef] [Google Scholar]
  52. Ness, M. K., Wheeler, A. J., McKinnon, K., et al. 2022, ApJ, 926, 144 [NASA ADS] [CrossRef] [Google Scholar]
  53. Netopil, M., Paunzen, E., Heiter, U., & Soubiran, C. 2016, A&A, 585, A150 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  54. Nidever, D. L., Holtzman, J. A., Allende Prieto, C., et al. 2015, AJ, 150, 173 [NASA ADS] [CrossRef] [Google Scholar]
  55. Pearson, K. 1901, London Edinb. Dublin Philos. Mag. J. Sci., 2, 559 [CrossRef] [Google Scholar]
  56. Price-Jones, N., Bovy, J., Webb, J. J., et al. 2020, MNRAS, 496, 5101 [NASA ADS] [CrossRef] [Google Scholar]
  57. Randich, S., Gilmore, G., Magrini, L., et al. 2022, A&A, 666, A121 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  58. Salaris, M., Pietrinferni, A., Piersimoni, A. M., & Cassisi, S. 2015, A&A, 583, A87 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  59. Soubiran, C., Cantat-Gaudin, T., Romero-Gómez, M., et al. 2018, A&A, 619, A155 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  60. Spina, L., Magrini, L., Sacco, G. G., et al. 2022, A&A, 668, A16 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  61. Steinmetz, M., Matijevič, G., Enke, H., et al. 2020, AJ, 160, 82 [NASA ADS] [CrossRef] [Google Scholar]
  62. Thrun, S. 1995, Proceedings of the 8th International Conference on Neural Information Processing Systems, NIPS’95 (Cambridge: MIT Press) [Google Scholar]
  63. Ting, Y.-S., & Weinberg, D. H. 2022, ApJ, 927, 209 [NASA ADS] [CrossRef] [Google Scholar]
  64. Ting, Y. S., Freeman, K. C., Kobayashi, C., De Silva, G. M., & Bland-Hawthorn, J. 2012, MNRAS, 421, 1231 [NASA ADS] [CrossRef] [Google Scholar]
  65. Vitali, S., Slumstrup, D., Jofré, P., et al. 2024, A&A, 687, A164 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  66. Witten, I. H., Frank, E., & Hall, M. A. 2011, Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. (San Francisco: Morgan Kaufmann Publishers Inc.) [Google Scholar]
  67. Zheng, J., & Rakovski, C. 2021, Data Sci. J., 20, 26 [CrossRef] [Google Scholar]

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