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Home All issues Volume 677 (September 2023) A&A, 677 (2023) A59 References
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Issue
A&A
Volume 677, September 2023
Article Number A59
Number of page(s) 59
Section Galactic structure, stellar clusters and populations
DOI https://doi.org/10.1051/0004-6361/202243690
Published online 06 September 2023
  1. Akaike, H. 1974, IEEE Trans. Autom. Control, 19, 716 [Google Scholar]
  2. Ankerst, M., Breunig, M. M., Kriegel, H.-P., & Sander, J. 1999, SIGMOD Rec., 28, 49 [CrossRef] [Google Scholar]
  3. Ashok Kumar, G. 2020, Int. J. Sci. Technol. Res., 9, 6 [Google Scholar]
  4. Astropy Collaboration (Robitaille, T. P., et al.) 2013, A&A, 558, A33 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  5. Astropy Collaboration (Price-Whelan, A. M., et al.) 2018, AJ, 156, 123 [Google Scholar]
  6. Azzalini, A., & Torelli, N. 2007, Stat. Comput., 17, 71 [CrossRef] [Google Scholar]
  7. Bailer-Jones, C. A. L., Rybizki, J., Fouesneau, M., Demleitner, M., & Andrae, R. 2021, AJ, 161, 147 [Google Scholar]
  8. Baraffe, I., Chabrier, G., Allard, F., & Hauschildt, P. H. 1998, A&A, 337, 403 [Google Scholar]
  9. Baraffe, I., Homeier, D., Allard, F., & Chabrier, G. 2015, A&A, 577, A42 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  10. Beccari, G., Boffin, H. M. J., & Jerabkova, T. 2020, MNRAS, 491, 2205 [Google Scholar]
  11. Benjamini, Y., & Hochberg, Y. 1995, J. R. Stat. Soc. Ser. B (Methodological), 57, 289 [Google Scholar]
  12. Bentley, J. L. 1975, Commun. ACM, 18, 509 [CrossRef] [Google Scholar]
  13. Biau, G., Chazal, F., Cohen-Steiner, D., Devroye, L., & Rodríguez, C. 2011, Electron. J. Stat., 5, 204 [CrossRef] [Google Scholar]
  14. Blaauw, A. 1946, Publ. Kapteyn Astron. Lab. Groningen, 52, 1 [NASA ADS] [Google Scholar]
  15. Blaauw, A. 1952, Bull. Astron. Inst. Netherlands, 11, 414 [NASA ADS] [Google Scholar]
  16. Blaauw, A. 1964a, ARA&A, 2, 213 [Google Scholar]
  17. Blaauw, A. 1964b, in The Galaxy and the Magellanic Clouds, ed. F. J. Kerr 20, 50 [NASA ADS] [Google Scholar]
  18. Boch, T., & Fernique, P. 2014, in Astronomical Data Analysis Software and Systems XXIII, eds. N. Manset, & P. Forshay, ASP Conf. Ser., 485, 277 [NASA ADS] [Google Scholar]
  19. Bonferroni, C. 1936, Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commericiali di Firenze, 8, 3 [Google Scholar]
  20. Bonnarel, F., Fernique, P., Bienaymé, O., et al. 2000, A&AS, 143, 33 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  21. Bouvier, J., Matt, S. P., Mohanty, S., et al. 2014, in Protostars and Planets VI, eds. H. Beuther, R. S. Klessen, C. P. Dullemond, & T. Henning, 433 [Google Scholar]
  22. Bouy, H., & Alves, J. 2015, A&A, 584, A26 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  23. Bovy, J. 2015, ApJS, 216, 29 [NASA ADS] [CrossRef] [Google Scholar]
  24. Breitschwerdt, D., Feige, J., Schulreich, M. M., et al. 2016, Nature, 532, 73 [NASA ADS] [CrossRef] [Google Scholar]
  25. Bressan, A., Marigo, P., Girardi, L., et al. 2012, MNRAS, 427, 127 [NASA ADS] [CrossRef] [Google Scholar]
  26. Briceño-Morales, G., & Chanamé, J. 2023, MNRAS, 522, 1288 [CrossRef] [Google Scholar]
  27. Brodersen, K. H., Ong, C. S., Stephan, K. E., & Buhmann, J. M. 2010, in 2010 20th International Conference on Pattern Recognition, 2010, 3121 [CrossRef] [Google Scholar]
  28. Brooke, T. Y., Huard, T. L., Bourke, T. L., et al. 2007, ApJ, 655, 364 [NASA ADS] [CrossRef] [Google Scholar]
  29. Burman, P., & Nolan, D. 1992, J. Multivariate Anal., 40, 132 [CrossRef] [Google Scholar]
  30. Burman, P., & Polonik, W. 2009, J. Multivariate Anal., 100, 1198 [CrossRef] [Google Scholar]
  31. Burrows, A., Hubbard, W. B., Lunine, J. I., & Liebert, J. 2001, Rev. Modern Phys., 73, 719 [NASA ADS] [CrossRef] [Google Scholar]
  32. Caliński, T., & Harabasz, J. 1974, Commun. Stat., 3, 1 [Google Scholar]
  33. Campello, R. J., Moulavi, D., & Sander, J. 2013, in Pacific-Asia Conference on Knowledge Discovery and Data Mining (Springer), 160 [Google Scholar]
  34. Cantat-Gaudin, T., & Anders, F. 2020, A&A, 633, A99 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  35. Cantat-Gaudin, T., Jordi, C., Vallenari, A., et al. 2018a, A&A, 618, A93 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  36. Cantat-Gaudin, T., Vallenari, A., Sordo, R., et al. 2018b, A&A, 615, A49 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  37. Cantat-Gaudin, T., Mapelli, M., Balaguer-Núñez, L., et al. 2019a, A&A, 621, A115 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  38. Cantat-Gaudin, T., Krone-Martins, A., Sedaghat, N., et al. 2019b, A&A, 624, A126 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  39. Castro-Ginard, A., Jordi, C., Luri, X., et al. 2018, A&A, 618, A59 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  40. Castro-Ginard, A., Jordi, C., Luri, X., Cantat-Gaudin, T., & Balaguer-Núñez, L. 2019, A&A, 627, A35 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  41. Castro-Ginard, A., Jordi, C., Luri, X., et al. 2020, A&A, 635, A45 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  42. Castro-Ginard, A., Jordi, C., Luri, X., et al. 2022, A&A, 661, A118 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  43. Celeux, G., Forbes, F., Robert, C. P., & Titterington, D. M. 2006, Bayesian Anal., 1, 651 [Google Scholar]
  44. Celeux, G., Frühwirth-Schnatter, S., & Robert, C. P. 2019, Handbook of Mixture Analysis, Chapman& Hall/CRC Handbooks of Modern Statistical Methods (CRC Press), 117 [Google Scholar]
  45. Chaudhuri, K., & Dasgupta, S. 2010, in Advances in Neural Information Processing Systems, eds. J. Lafferty, C. Williams, J. Shawe-Taylor, R. Zemel, & A. Culotta (Curran Associates, Inc.), 23 [Google Scholar]
  46. Chaudhuri, K., Dasgupta, S., Kpotufe, S., & von Luxburg, U. 2014, IEEE Trans. Inf. Theor., 60, 7900 [CrossRef] [Google Scholar]
  47. Chazal, F., Guibas, L. J., Oudot, S. Y., & Skraba, P. 2013, J. ACM, 60 [CrossRef] [Google Scholar]
  48. Chen, B., D’Onghia, E., Alves, J., & Adamo, A. 2020, A&A, 643, A114 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  49. Cheng, Y. 1995, IEEE Trans. Pattern Analy. Mach. Intell., 17, 790 [CrossRef] [Google Scholar]
  50. Chen, Y., Girardi, L., Bressan, A., et al. 2014, MNRAS, 444, 2525 [Google Scholar]
  51. Chen, Y., Bressan, A., Girardi, L., et al. 2015, MNRAS, 452, 1068 [Google Scholar]
  52. Chronis, P., Athanasiou, S., & Skiadopoulos, S. 2019, in 2019 IEEE International Conference on Data Mining (ICDM), 91 [CrossRef] [Google Scholar]
  53. Comaniciu, D., & Meer, P. 2002, IEEE Trans. Pattern Anal. Mach. Intell., 24, 603 [CrossRef] [Google Scholar]
  54. Cornuéjols, A., Wemmert, C., Gançarski, P., & Bennani, Y. 2018, Inf. Fusion, 39, 81 [CrossRef] [Google Scholar]
  55. Coronado, J., Fürnkranz, V., & Rix, H.-W. 2022, ApJ, 928, 70 [NASA ADS] [CrossRef] [Google Scholar]
  56. Correa, C., & Lindstrom, P. 2011, IEEE Trans. Visual. Comput. Graphics, 17, 1852 [CrossRef] [Google Scholar]
  57. Crundall, T. D., Ireland, M. J., Krumholz, M. R., et al. 2019, MNRAS, 489, 3625 [Google Scholar]
  58. Damiani, F., Prisinzano, L., Pillitteri, I., Micela, G., & Sciortino, S. 2019, A&A, 623, A112 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  59. Dasgupta, S., & Kpotufe, S. 2014, in Advances in Neural Information Processing Systems, eds. Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, & K. Q. Weinberger (Curran Associates, Inc.), 27 [Google Scholar]
  60. de Bruijne, J. H. J. 1999, MNRAS, 310, 585 [NASA ADS] [CrossRef] [Google Scholar]
  61. de Geus, E. J. 1992, A&A, 262, 258 [NASA ADS] [Google Scholar]
  62. de Geus, E. J., de Zeeuw, P. T., & Lub, J. 1989, A&A, 216, 44 [NASA ADS] [Google Scholar]
  63. Dempster, A. P., Laird, N. M., & Rubin, D. B. 1977, J. R. Stat. Soc. Series B Stat. Methodol., 39, 1 [Google Scholar]
  64. de Zeeuw, P. T., Hoogerwerf, R., de Bruijne, J. H. J., Brown, A. G. A., & Blaauw, A. 1999, AJ, 117, 354 [Google Scholar]
  65. Diehl, R., Lang, M. G., Martin, P., et al. 2010, A&A, 522, A51 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  66. Dieterich, S. B., Henry, T. J., Jao, W.-C., et al. 2014, AJ, 147, 94 [NASA ADS] [CrossRef] [Google Scholar]
  67. Ding, J., Shah, S., & Condon, A. 2016, Bioinformatics, 32, 2567 [CrossRef] [Google Scholar]
  68. Dobbie, P. D., Lodieu, N., & Sharp, R. G. 2010, MNRAS, 409, 1002 [Google Scholar]
  69. Edelsbrunner, H., Letscher, D., & Zomorodian, A. 2000, in Proceedings 41st Annual Symposium on Foundations of Computer Science, 454 [CrossRef] [Google Scholar]
  70. Esplin, T. L., & Luhman, K. L. 2022, AJ, 163, 64 [NASA ADS] [CrossRef] [Google Scholar]
  71. Ester, M., Kriegel, H. P., Sander, J., & Xu, X. 1996, in Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, KDD’96 (AAAI Press), 226 [Google Scholar]
  72. Evans, D. W., Riello, M., De Angeli, F., et al. 2018, A&A, 616, A4 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  73. Feigelson, E. D., & Montmerle, T. 1999, ARA&A, 37, 363 [Google Scholar]
  74. Feng, Y., & Hamerly, G. 2007, in Advances in Neural Information Processing Systems, eds. B. Schölkopf, J. Platt, & T. Hoffman, 19 (MIT Press) [Google Scholar]
  75. Fernández, D., Figueras, F., & Torra, J. 2008, A&A, 480, 735 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  76. Fisher, R. A. 1934, Breakthroughs in Statistics (Springer), 66 [Google Scholar]
  77. Forbes, J. C., Alves, J., & Lin, D. N. C. 2021, Nat. Astron., 5, 1009 [NASA ADS] [CrossRef] [Google Scholar]
  78. Francis, C., & Anderson, E. 2009, New A, 14, 615 [NASA ADS] [CrossRef] [Google Scholar]
  79. Freytag, B., Allard, F., Ludwig, H. G., Homeier, D., & Steffen, M. 2010, A&A, 513, A19 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  80. Freytag, B., Steffen, M., Ludwig, H. G., et al. 2012, J. Comput. Phys., 231, 919 [Google Scholar]
  81. Frühwirth-Schnatter, S., Celeux, G., & Robert, C. 2019, in Handbook of Mixture Analysis, Chapman& Hall/CRC Handbooks of Modern Statistical Methods (CRC Press) [Google Scholar]
  82. Fürnkranz, V., Meingast, S., & Alves, J. 2019, A&A, 624, L11 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  83. Gabriel, K. R., & Sokal, R. R. 1969, System. Biol., 18, 259 [Google Scholar]
  84. Gagné, J., & Faherty, J. K. 2018, ApJ, 862, 138 [Google Scholar]
  85. Gagné, J., Lafrenière, D., Doyon, R., Malo, L., & Artigau, É. 2014, ApJ, 783, 121 [Google Scholar]
  86. Gagné, J., Mamajek, E. E., Malo, L., et al. 2018a, ApJ, 856, 23 [Google Scholar]
  87. Gagné, J., Roy-Loubier, O., Faherty, J. K., Doyon, R., & Malo, L. 2018b, ApJ, 860, 43 [Google Scholar]
  88. Gagné, J., Faherty, J. K., & Mamajek, E. E. 2018c, ApJ, 865, 136 [Google Scholar]
  89. Gaia Collaboration (Brown, A. G. A., et al.) 2016, A&A, 595, A2 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  90. Gaia Collaboration (Brown, A. G. A., et al.) 2018, A&A, 616, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  91. Gaia Collaboration (Brown, A. G. A., et al.) 2021, A&A, 649, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  92. Gaia Collaboration (Vallenari, A., et al.) 2023a, A&A, 674, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  93. Gaia Collaboration (Arenou, F., et al.) 2023b, A&A, 674, A34 [CrossRef] [EDP Sciences] [Google Scholar]
  94. Galli, P. A. B., Joncour, I., & Moraux, E. 2018, MNRAS, 477, L50 [Google Scholar]
  95. Galli, P. A. B., Bouy, H., Olivares, J., et al. 2020a, A&A, 643, A148 [EDP Sciences] [Google Scholar]
  96. Galli, P. A. B., Bouy, H., Olivares, J., et al. 2020b, A&A, 634, A98 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  97. Galli, P. A. B., Bouy, H., Olivares, J., et al. 2021a, A&A, 654, A122 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  98. Galli, P. A. B., Bouy, H., Olivares, J., et al. 2021b, A&A, 646, A46 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  99. Ghrist, R. 2008, Bull. Am. Math. Soc., 45, 61 [Google Scholar]
  100. Goldman, B., Röser, S., Schilbach, E., Moór, A. C., & Henning, T. 2018, ApJ, 868, 32 [Google Scholar]
  101. Grasser, N., Ratzenböck, S., Alves, J., et al. 2021, A&A, 652, A2 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  102. Gritschneder, M., & Lin, D. N. C. 2012, ApJ, 754, L13 [NASA ADS] [CrossRef] [Google Scholar]
  103. Großschedl, J. E., Alves, J., Meingast, S., & Herbst-Kiss, G. 2021, A&A, 647, A91 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  104. Hamerly, G., & Elkan, C. 2004, in Advances in Neural Information Processing, eds. S. Thrun, L. Saul, & B. Schölkopf, 16 (MIT Press) [Google Scholar]
  105. Hartigan, J. A. 1975, Clustering Algorithms, 99th edn. (USA: John Wiley& Sons, Inc.) [Google Scholar]
  106. Hartigan, J. A., & Hartigan, P. M. 1985, Ann. Stat., 13, 70 [Google Scholar]
  107. He, Z., Wang, K., Luo, Y., et al. 2022, ApJS, 262, 7 [NASA ADS] [CrossRef] [Google Scholar]
  108. Hogg, D. W., Bovy, J., & Lang, D. 2010, ArXiv e-prints [arXiv:1008.4686] [Google Scholar]
  109. Hu, X., & Xu, L. 2003, in Intelligent Data Engineering and Automated Learning, eds. J. Liu, Y. M. Cheung, & H. Yin (Berlin, Heidelberg: Springer), 195 [CrossRef] [Google Scholar]
  110. Hubert, L., & Arabie, P. 1985, J. Classification, 2, 193 [CrossRef] [Google Scholar]
  111. Hunt, E. L., & Reffert, S. 2021, A&A, 646, A104 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  112. Hunt, E. L., & Reffert, S. 2023, A&A, 673, A114 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  113. Hunter, J. D. 2007, Comput. Sci. Eng., 9, 90 [NASA ADS] [CrossRef] [Google Scholar]
  114. Jain, A. K., Murty, M. N., & Flynn, P. J. 1999, ACM Comput. Surveys, 31, 264 [CrossRef] [Google Scholar]
  115. Jaromczyk, J., & Toussaint, G. 1992, Proc. IEEE, 80, 1502 [CrossRef] [Google Scholar]
  116. Jerabkova, T., Boffin, H. M. J., Beccari, G., & Anderson, R. I. 2019, MNRAS, 489, 4418 [Google Scholar]
  117. Jerabkova, T., Boffin, H. M. J., Beccari, G., et al. 2021, A&A, 647, A137 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  118. Johnson, S. C. 1967, Psychometrika, 32, 241 [CrossRef] [Google Scholar]
  119. Kalogeratos, A., & Likas, A. 2012, in Advances in Neural Information Processing Systems, eds. F. Pereira, C. J. C. Burges, L. Bottou, & K. Q. Weinberger (Curran Associates, Inc.), 25 [Google Scholar]
  120. Kamdar, H., Conroy, C., Ting, Y.-S., & El-Badry, K. 2021, ApJ, 922, 49 [NASA ADS] [CrossRef] [Google Scholar]
  121. Kapteyn, J. C. 1914, ApJ, 40, 43 [NASA ADS] [CrossRef] [Google Scholar]
  122. Katz, D., Sartoretti, P., Guerrier, A., et al. 2023, A&A, 674, A5 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  123. Kerr, F. J., & Lynden-Bell, D. 1986, MNRAS, 221, 1023 [NASA ADS] [CrossRef] [Google Scholar]
  124. Kerr, R. M. P., Rizzuto, A. C., Kraus, A. L., & Offner, S. S. R. 2021, ApJ, 917, 23 [NASA ADS] [CrossRef] [Google Scholar]
  125. Kervella, P., Arenou, F., & Thévenin, F. 2022, A&A, 657, A7 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  126. Kharchenko, N. V., Piskunov, A. E., Schilbach, E., Röser, S., & Scholz, R. D. 2013, A&A, 558, A53 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  127. Kirkpatrick, D. G., & Radke, J. D. 1985, in Machine Intelligence and Pattern Recognition, ed. G. T. Toussaint (North-Holland), Comput. Geom., 2, 217 [Google Scholar]
  128. Koontz W. L. G., Narendra P. M., & Fukunaga K. 1976, IEEE Trans. Comput., 25, 936 [CrossRef] [Google Scholar]
  129. Kounkel, M., & Covey, K. 2019, AJ, 158, 122 [Google Scholar]
  130. Kounkel, M., Covey, K., & Stassun, K. G. 2020, AJ, 160, 279 [NASA ADS] [CrossRef] [Google Scholar]
  131. Kpotufe, S., & von Luxburg, U. 2011, in Proceedings of the 28th International Conference on International Conference on Machine Learning, ICML’11 (Madison, WI, USA: Omnipress), 225 [Google Scholar]
  132. Krone-Martins, A., & Moitinho, A. 2014, A&A, 561, A57 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  133. Kuhn, M. A., & Feigelson, E. D. 2019, Handbook of Mixture Analysis, Chapman& Hall/CRC Handbooks of Modern Statistical Methods (CRC Press), 463 [Google Scholar]
  134. Kushniruk, I., Schirmer, T., & Bensby, T. 2017, A&A, 608, A73 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  135. Lange, K. L., Little, R. J. A., & Taylor, J. M. G. 1989, J. Am. Stat. Assoc., 84, 881 [Google Scholar]
  136. Leike, R. H., Glatzle, M., & Enßlin, T. A. 2020, A&A, 639, A138 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  137. Lépine, J. R. D., & Sartori, M. J. 2003, Astrophys. Space Sci. Libr., 299, 63 [Google Scholar]
  138. Lifshitz, L., & Pizer, S. 1990, IEEE Trans. Pattern Anal. Mach. Intell., 12, 529 [CrossRef] [Google Scholar]
  139. Lindegren, L., Hernández, J., Bombrun, A., et al. 2018, A&A, 616, A2 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  140. Lindegren, L., Klioner, S. A., Hernández, J., et al. 2021, A&A, 649, A2 [EDP Sciences] [Google Scholar]
  141. Liu, Y., & Xie, J. 2020, J. Am. Stat. Assoc., 115, 393 [CrossRef] [Google Scholar]
  142. Lombardi, M., Alves, J., & Lada, C. J. 2006, A&A, 454, 781 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  143. Luhman, K. L. 2022, AJ, 163, 24 [NASA ADS] [CrossRef] [Google Scholar]
  144. Luhman, K. L., & Esplin, T. L. 2020, AJ, 160, 44 [Google Scholar]
  145. Luri, X., Brown, A. G. A., Sarro, L. M., et al. 2018, A&A, 616, A9 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  146. MacQueen, J. B. 1967, in Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, eds. L. M. L. Cam, & J. Neyman (University of California Press), 1, 281 [Google Scholar]
  147. Magnani, L., Blitz, L., & Mundy, L. 1985, ApJ, 295, 402 [NASA ADS] [CrossRef] [Google Scholar]
  148. Makarov, V. V. 2007a, ApJS, 169, 105 [NASA ADS] [CrossRef] [Google Scholar]
  149. Makarov, V. V. 2007b, ApJ, 670, 1225 [NASA ADS] [CrossRef] [Google Scholar]
  150. Malsiner-Walli, G., Frühwirth-Schnatter, S., & Grün, B. 2016, Stat. comput., 26, 303 [CrossRef] [Google Scholar]
  151. Mamajek, E. E., & Feigelson, E. D. 2001, in Young Stars Near Earth: Progress and Prospects, eds. T. R. Jayawardhana, & T. Greene, ASP Conf. Ser., 244, 104 [NASA ADS] [Google Scholar]
  152. Mamajek, E. E., Lawson, W. A., & Feigelson, E. D. 1999, ApJ, 516, L77 [Google Scholar]
  153. Mamajek, E. E., Lawson, W. A., & Feigelson, E. D. 2000, ApJ, 544, 356 [NASA ADS] [CrossRef] [Google Scholar]
  154. Marigo, P., Girardi, L., Bressan, A., et al. 2017, ApJ, 835, 77 [Google Scholar]
  155. Matthews, B. 1975, Biochim. Biophys. Acta (BBA) - Protein Structure, 405, 442 [CrossRef] [Google Scholar]
  156. Maurus, S., & Plant, C. 2016, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16 (New York, NY, USA: Association for Computing Machinery), 1055 [CrossRef] [Google Scholar]
  157. McInnes, L., Healy, J., & Astels, S. 2017, J. Open Source Softw., 2, 205 [NASA ADS] [CrossRef] [Google Scholar]
  158. Meingast, S., & Alves, J. 2019, A&A, 621, L3 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  159. Meingast, S., Alves, J., & Fürnkranz, V. 2019, A&A, 622, L13 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  160. Meingast, S., Alves, J., & Rottensteiner, A. 2021, A&A, 645, A84 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  161. Merloni, A., Nandra, K., & Predehl, P. 2020, Nat. Astron., 4, 634 [Google Scholar]
  162. Miret-Roig, N., Galli, P. A. B., Brandner, W., et al. 2020, A&A, 642, A179 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  163. Miret-Roig, N., Galli, P. A. B., Olivares, J., et al. 2022a, A&A, 667, A163 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  164. Miret-Roig, N., Bouy, H., Raymond, S. N., et al. 2022b, Nat. Astron., 6, 89 [NASA ADS] [CrossRef] [Google Scholar]
  165. Muller, D. W., & Sawitzki, G. 1991, J. Am. Stat. Assoc., 86, 738 [Google Scholar]
  166. Müller, P., & Mitra, R. 2013, Bayesian Anal., 8, 269 [Google Scholar]
  167. Neuhäuser, R. 1997, Science, 276, 1363 [CrossRef] [Google Scholar]
  168. Neuhäuser, R., Gießler, F., & Hambaryan, V. V. 2020, MNRAS, 498, 899 [CrossRef] [Google Scholar]
  169. Nocedal, J., & Wright, S. J. 1999, in Numerical Optimization (New York, NY: Springer) [CrossRef] [Google Scholar]
  170. Ochsenbein, F., Bauer, P., & Marcout, J. 2000, A&AS, 143, 23 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  171. Oh, S., Price-Whelan, A. M., Hogg, D. W., Morton, T. D., & Spergel, D. N. 2017, AJ, 153, 257 [NASA ADS] [CrossRef] [Google Scholar]
  172. Ohnaka, K., Hofmann, K. H., Schertl, D., et al. 2013, A&A, 555, A24 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  173. Olivares, J., Bouy, H., Sarro, L. M., et al. 2021, A&A, 649, A159 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  174. Pagani, L., Lagache, G., Bacmann, A., et al. 2003, A&A, 406, L59 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  175. Pagani, L., Bacmann, A., Motte, F., et al. 2004, A&A, 417, 605 [CrossRef] [EDP Sciences] [Google Scholar]
  176. Pagani, L., Pardo, J. R., Apponi, A. J., Bacmann, A., & Cabrit, S. 2005, A&A, 429, 181 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  177. Pecaut, M. J., & Mamajek, E. E. 2013, ApJS, 208, 9 [Google Scholar]
  178. Pecaut, M. J., & Mamajek, E. E. 2016, MNRAS, 461, 794 [Google Scholar]
  179. Pecaut, M. J., Mamajek, E. E., & Bubar, E. J. 2012, ApJ, 746, 154 [Google Scholar]
  180. Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, J. Mach. Learn. Res., 12, 2825 [Google Scholar]
  181. Penoyre, Z., Belokurov, V., & Evans, N. W. 2022a, MNRAS, 513, 2437 [NASA ADS] [CrossRef] [Google Scholar]
  182. Penoyre, Z., Belokurov, V., & Evans, N. W. 2022b, MNRAS, 513, 5270 [Google Scholar]
  183. Perryman, M. A. C., Lindegren, L., Kovalevsky, J., et al. 1997, A&A, 323, L49 [Google Scholar]
  184. Perryman, M. A. C., Brown, A. G. A., Lebreton, Y., et al. 1998, A&A, 331, 81 [NASA ADS] [Google Scholar]
  185. Plotly, T. I. 2015, Collaborative Data Science, Montreal, QC, https://plot.ly [Google Scholar]
  186. Pöppel, W. G. L., Bajaja, E., Arnal, E. M., & Morras, R. 2010, A&A, 512, A83 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  187. Preibisch, T., & Mamajek, E. 2008, in Handbook of Star Forming Regions, Volume II, ed. B. Reipurth, 5, 235 [NASA ADS] [Google Scholar]
  188. Preibisch, T., & Zinnecker, H. 1999, AJ, 117, 2381 [NASA ADS] [CrossRef] [Google Scholar]
  189. Randich, S., Schmitt, J. H. M. M., Prosser, C. F., & Stauffer, J. R. 1995, A&A, 300, 134 [NASA ADS] [Google Scholar]
  190. Ratzenböck, S., Meingast, S., Alves, J., Möller, T., & Bomze, I. 2020, A&A, 639, A64 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  191. Ratzenböck, S., Großschedl, J. E., Alves, J., et al. 2023, A&A, in press, https://doi.org/10.1051/0004-6361/202346901 [Google Scholar]
  192. Reininghaus, J., Kotava, N., Guenther, D., et al. 2011, IEEE Trans. Visual. Comput. Graphics, 17, 2045 [CrossRef] [Google Scholar]
  193. Richardson, S., & Green, P. J. 1997, J. R. Stat. Soc. Ser. B (Stat. Methodol.), 59, 731 [CrossRef] [Google Scholar]
  194. Riedel, A. R., Blunt, S. C., Lambrides, E. L., et al. 2017, AJ, 153, 95 [NASA ADS] [CrossRef] [Google Scholar]
  195. Riello, M., De Angeli, F., Evans, D. W., et al. 2021, A&A, 649, A3 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  196. Rizzuto, A. C., Ireland, M. J., & Robertson, J. G. 2011, MNRAS, 416, 3108 [Google Scholar]
  197. Roccatagliata, V., Sacco, G. G., Franciosini, E., & Randich, S. 2018, A&A, 617, L4 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  198. Román-Zúñiga, C. G., Lada, C. J., Muench, A., & Alves, J. F. 2007, ApJ, 664, 357 [CrossRef] [Google Scholar]
  199. Román-Zúñiga, C. G., Alves, J. F., Lada, C. J., & Lombardi, M. 2010, ApJ, 725, 2232 [Google Scholar]
  200. Röser, S., Schilbach, E., Goldman, B., et al. 2018, A&A, 614, A81 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  201. Röser, S., Schilbach, E., & Goldman, B. 2019, A&A, 621, L2 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  202. Rybizki, J., Demleitner, M., Bailer-Jones, C., et al. 2020, PASP, 132, 074501 [Google Scholar]
  203. Rybizki, J., Green, G. M., Rix, H.-W., et al. 2022, MNRAS, 510, 2597 [NASA ADS] [CrossRef] [Google Scholar]
  204. Salvatier, J., Wiecki, T. V., & Fonnesbeck, C. 2016, PeerJ Comput. Sci., 2, e55 [Google Scholar]
  205. Sarro, L. M., Bouy, H., Berihuete, A., et al. 2014, A&A, 563, A45 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  206. Sartori, M. J., Lépine, J. R. D., & Dias, W. S. 2003, A&A, 404, 913 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  207. Schmitt, J. H. M. M. 1997, A&A, 318, 215 [NASA ADS] [Google Scholar]
  208. Schmitt, J. H. M. M., Czesla, S., Freund, S., Robrade, J., & Schneider, P. C. 2022, A&A, 661, A40 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  209. Schönrich, R., Binney, J., & Dehnen, W. 2010, MNRAS, 403, 1829 [NASA ADS] [CrossRef] [Google Scholar]
  210. Schwarz, G. 1978, Ann. Stat., 6, 461 [Google Scholar]
  211. Shaffer, J. P. 1995, Ann. Rev. Psychol., 46, 561 [CrossRef] [Google Scholar]
  212. Sim, G., Lee, S. H., Ann, H. B., & Kim, S. 2019, J. Korean Astron. Soc., 52, 145 [NASA ADS] [Google Scholar]
  213. Squicciarini, V., Gratton, R., Bonavita, M., & Mesa, D. 2021, MNRAS, 507, 1381 [NASA ADS] [CrossRef] [Google Scholar]
  214. Stauffer, J. R., Hartmann, L. W., Prosser, C. F., et al. 1997, ApJ, 479, 776 [NASA ADS] [CrossRef] [Google Scholar]
  215. Steinacker, J., Pagani, L., Bacmann, A., & Guieu, S. 2010, A&A, 511, A9 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  216. Steinacker, J., Andersen, M., Thi, W. F., et al. 2015, A&A, 582, A70 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  217. Strehl, A., & Ghosh, J. 2002, J. Mach. Learn. Res., 3, 583 [Google Scholar]
  218. Stuetzle, W., & Nugent, R. 2010, J. Comput. Graphical Stat., 19, 397 [CrossRef] [Google Scholar]
  219. Taylor, M. B. 2005, in Astronomical Data Analysis Software and Systems XIV, eds. P. Shopbell, M. Britton, & R. Ebert, ASP Conf. Ser., 347, 29 [Google Scholar]
  220. Teixeira, P. S., Scholz, A., & Alves, J. 2020, A&A, 642, A86 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  221. Torra, F., Castañeda, J., Fabricius, C., et al. 2021, A&A, 649, A10 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  222. van der Walt, S., Colbert, S. C., & Varoquaux, G. 2011, Comput. Sci. Eng., 13, 22 [Google Scholar]
  223. van Leeuwen, F. 2007, A&A, 474, 653 [CrossRef] [EDP Sciences] [Google Scholar]
  224. van Leeuwen, F. 2009, A&A, 497, 209 [CrossRef] [EDP Sciences] [Google Scholar]
  225. Vedaldi, A., & Soatto, S. 2008, in Computer Vision - ECCV 2008, eds. D. Forsyth, P. Torr, & A. Zisserman (Berlin, Heidelberg: Springer), 705 [CrossRef] [Google Scholar]
  226. Vega-Pons, S., & Ruiz-Shulcloper, J. 2011, Int. J. Pattern Recogn. Artif. Intell., 25, 337 [CrossRef] [Google Scholar]
  227. Vehtari, A., Gelman, A., & Gabry, J. 2017, Stat. Comput., 27, 1413 [Google Scholar]
  228. Villa Vélez, J. A., Brown, A. G. A., & Kenworthy, M. A. 2018, Res. Notes Am. Astron. Soc., 2, 58 [Google Scholar]
  229. Vinh, N. X., Epps, J., & Bailey, J. 2010, J. Mach. Learn. Res., 11, 2837 [Google Scholar]
  230. Wang, K., & Ge, Y. 2021, Astrophysics Source Code Library [record ascl:2102.002] [Google Scholar]
  231. Watanabe, S. 2010, J. Mach. Learn. Res., 11, 3571 [Google Scholar]
  232. Wenger, M., Ochsenbein, F., Egret, D., et al. 2000, A&AS, 143, 9 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  233. Wishart, D. 1969, in Proceedings of the Colloquium in Numerical Taxonomy, ed. A. J. Cole (New York: Academic Press), 282 [Google Scholar]
  234. Witkin, A. P. 1987, in Readings in Computer Vision, eds. M. A. Fischler, & O. Firschein (San Francisco, CA: Morgan Kaufmann), 329 [Google Scholar]
  235. Wright, N. J., & Mamajek, E. E. 2018, MNRAS, 476, 381 [Google Scholar]
  236. Zari, E., Brown, A. G. A., de Bruijne, J., Manara, C. F., & de Zeeuw, P. T. 2017, A&A, 608, A148 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  237. Zari, E., Brown, A. G. A., & de Zeeuw, P. T. 2019, A&A, 628, A123 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  238. Zari, E., Rix, H. W., Frankel, N., et al. 2021, A&A, 650, A112 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  239. Žerjal, M., Ireland, M. J., Crundall, T. D., Krumholz, M. R., & Rains, A. D. 2023, MNRAS, 519, 3992 [CrossRef] [Google Scholar]
  240. Zomorodian, A., & Carlsson, G. 2005, Discrete Comput. Geom., 33, 249 [CrossRef] [Google Scholar]
  241. Zucker, C., Speagle, J. S., Schlafly, E. F., et al. 2019, ApJ, 879, 125 [NASA ADS] [CrossRef] [Google Scholar]
  242. Zucker, C., Goodman, A., Alves, J., et al. 2021, ApJ, 919, 35 [NASA ADS] [CrossRef] [Google Scholar]
  243. Zucker, C., Goodman, A. A., Alves, J., et al. 2022, Nature, 601, 334 [NASA ADS] [CrossRef] [Google Scholar]

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