Open Access
Issue
A&A
Volume 668, December 2022
Article Number A95
Number of page(s) 23
Section Galactic structure, stellar clusters and populations
DOI https://doi.org/10.1051/0004-6361/202244453
Published online 08 December 2022
  1. Ambikasaran, S., Foreman-Mackey, D., Greengard, L., Hogg, D. W., & O’Neil, M. 2015, IEEE Trans. Pattern Anal. Machine Intell., 38, 252 [Google Scholar]
  2. Anders, F., Khalatyan, A., Queiroz, A. B. A., et al. 2022, A&A, 658, A91 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  3. Antoja, T., Helmi, A., Romero-Gómez, M., et al. 2018, Nature, 561, 360 [Google Scholar]
  4. Bennett, M., & Bovy, J. 2019, MNRAS, 482, 1417 [NASA ADS] [CrossRef] [Google Scholar]
  5. Binney, J., & Merrifield, M. 1998, Galactic Astronomy (Princeton University Press) [Google Scholar]
  6. Bland-Hawthorn, J., Sharma, S., Tepper-Garcia, T., et al. 2019, MNRAS, 486, 1167 [NASA ADS] [CrossRef] [Google Scholar]
  7. Boubert, D., & Everall, A. 2020, MNRAS, 497, 4246 [NASA ADS] [CrossRef] [Google Scholar]
  8. Cantat-Gaudin, T., Jordi, C., Vallenari, A., et al. 2018, A&A, 618, A93 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  9. Cantat-Gaudin, T., Fouesneau, M., Rix, H.-W., et al. 2022, A&A, in press, https://doi.org/10.1051/0004-6361/202244784 [Google Scholar]
  10. Carlin, J. L., DeLaunay, J., Newberg, H. J., et al. 2013, ApJ, 777, L5 [Google Scholar]
  11. Everall, A., & Boubert, D. 2022, MNRAS, 509, 6205 [Google Scholar]
  12. Everall, A., Belokurov, V., Evans, N. W., Boubert, D., & Grand, R. J. J. 2022a, MNRAS, 511, 3863 [NASA ADS] [CrossRef] [Google Scholar]
  13. Everall, A., Evans, N. W., Belokurov, V., Boubert, D., & Grand, R. J. J. 2022b, MNRAS, 511, 2390 [NASA ADS] [CrossRef] [Google Scholar]
  14. Friske, J. K. S., & Schönrich, R. 2019, MNRAS, 490, 5414 [Google Scholar]
  15. Gaia Collaboration (Prusti, T., et al.) 2016, A&A, 595, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  16. Gaia Collaboration (Katz, D., et al.) 2018, A&A, 616, A11 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  17. Gaia Collaboration (Drimmel, R., et al.) 2022a, A&A, in press, https://doi.org/10.1051/0004-6361/202243797 [Google Scholar]
  18. Gaia Collaboration (Vallenari, A., et al.) 2022b, A&A, in presss, https://doi.org/10.1051/0004-6361/202243940 [Google Scholar]
  19. Górski, K. M., Hivon, E., Banday, A. J., et al. 2005, ApJ, 622, 759 [Google Scholar]
  20. Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357 [NASA ADS] [CrossRef] [Google Scholar]
  21. Hunt, J. A. S., Price-Whelan, A. M., Johnston, K. V., & Darragh-Ford, E. 2022, MNRAS, 516, L7 [NASA ADS] [CrossRef] [Google Scholar]
  22. Hunt, J. A. S., Stelea, I. A., Johnston, K. V., et al. 2021, MNRAS, 508, 1459 [NASA ADS] [CrossRef] [Google Scholar]
  23. Hunter, J. D. 2007, Comput. Sci. Eng., 9, 90 [NASA ADS] [CrossRef] [Google Scholar]
  24. Kingma, D. P., & Ba, J. 2014, ArXiv e-prints [arXiv:1412.6980] [Google Scholar]
  25. Kordopatis, G., Schultheis, M., McMillan, P. J., et al. 2022, A&A, in press, https://doi.org/10.1051/0004-6361/202244283 [Google Scholar]
  26. Kumar, A., Ghosh, S., Kataria, S. K., Das, M., & Debattista, V. P. 2022, MNRAS, 516, 1114 [NASA ADS] [CrossRef] [Google Scholar]
  27. Laporte, C. F. P., Minchev, I., Johnston, K. V., & Gómez, F. A. 2019, MNRAS, 485, 3134 [Google Scholar]
  28. Li, H., & Widrow, L. M. 2021, MNRAS, 503, 1586 [CrossRef] [Google Scholar]
  29. Martinez-Medina, L., Pérez-Villegas, A., & Peimbert, A. 2022, MNRAS, 512, 1574 [NASA ADS] [CrossRef] [Google Scholar]
  30. Mathur, S. D. 1990, MNRAS, 243, 529 [NASA ADS] [Google Scholar]
  31. McMillan, P. J. 2016, MNRAS, 465, 76 [Google Scholar]
  32. Monari, G., Famaey, B., & Siebert, A. 2015, MNRAS, 452, 747 [NASA ADS] [CrossRef] [Google Scholar]
  33. Monari, G., Famaey, B., & Siebert, A. 2016a, MNRAS, 457, 2569 [Google Scholar]
  34. Monari, G., Famaey, B., Siebert, A., et al. 2016b, MNRAS, 461, 3835 [Google Scholar]
  35. Nelson, P., & Widrow, L. M. 2022, MNRAS, 516, 5429 [NASA ADS] [CrossRef] [Google Scholar]
  36. Oort, J. H. 1932, Bull. Astron. Inst. Neth., 6, 249 [NASA ADS] [Google Scholar]
  37. Poggio, E., Drimmel, R., Andrae, R., et al. 2020, Nature Astron., 4, 590 [NASA ADS] [CrossRef] [Google Scholar]
  38. Rasmussen, C. E., & Williams, C. K. I. 2005, Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) (The MIT Press) [Google Scholar]
  39. Reid, M. J., Menten, K. M., Brunthaler, A., et al. 2014, ApJ, 783, 130 [Google Scholar]
  40. Reid, M. J., Menten, K. M., Brunthaler, A., et al. 2019, ApJ, 885, 131 [Google Scholar]
  41. Rybizki, J., Rix, H.-W., Demleitner, M., Bailer-Jones, C. A. L., & Cooper, W. J. 2021, MNRAS, 500, 397 [Google Scholar]
  42. Schönrich, R., & Dehnen, W. 2018, MNRAS, 478, 3809 [Google Scholar]
  43. Sellwood, J. A. 2013, in Planets, Stars and Stellar Systems. Volume 5: Galactic Structure and Stellar Populations, eds. T. D. Oswalt, & G. Gilmore, 5, 923 [Google Scholar]
  44. Titsias, M. 2009, J. Mach. Learn. Res. Proc. Track, 5, 567 [Google Scholar]
  45. Weinberg, M. D. 1991, ApJ, 373, 391 [NASA ADS] [CrossRef] [Google Scholar]
  46. Widmark, A., Laporte, C. F. P., de Salas, P. F., & Monari, G. 2021, A&A, 653, A86 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  47. Widmark, A., Hunt, J. A. S., Laporte, C. F. P., & Monari, G. 2022a, A&A, 663, A16 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  48. Widmark, A., Laporte, C. F. P., & Monari, G. 2022b, A&A, 663, A15 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  49. Widrow, L. M., & Bonner, G. 2015, MNRAS, 450, 266 [NASA ADS] [CrossRef] [Google Scholar]
  50. Widrow, L. M., Gardner, S., Yanny, B., Dodelson, S., & Chen, H.-Y. 2012, ApJ, 750, L41 [Google Scholar]
  51. Williams, M. E. K., Steinmetz, M., Binney, J., et al. 2013, MNRAS, 436, 101 [Google Scholar]
  52. Xu, Y., Li, J. J., Reid, M. J., et al. 2013, ApJ, 769, 15 [Google Scholar]
  53. Xu, Y., Newberg, H. J., Carlin, J. L., et al. 2015, ApJ, 801, 105 [Google Scholar]
  54. Yanny, B., & Gardner, S. 2013, ApJ, 777, 91 [Google Scholar]

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