Free Access
Issue
A&A
Volume 636, April 2020
Article Number A75
Number of page(s) 25
Section Galactic structure, stellar clusters and populations
DOI https://doi.org/10.1051/0004-6361/201936866
Published online 21 April 2020
  1. Abadi, M., Agarwal, A., Barham, P., et al. 2015, TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, Software available from tensorflow.org, https://www.tensorflow.org [Google Scholar]
  2. Astropy Collaboration (Price-Whelan, A. M., et al.) 2018, AJ, 156, 123 [NASA ADS] [CrossRef] [Google Scholar]
  3. Bahcall, J. N., & Soneira, R. M. 1980, ApJS, 44, 73 [NASA ADS] [CrossRef] [Google Scholar]
  4. Behroozi, P. S., Wechsler, R. H., & Wu, H.-Y. 2013, ApJ, 762, 109 [NASA ADS] [CrossRef] [Google Scholar]
  5. Belokurov, V., Evans, N. W., Irwin, M. J., et al. 2007, ApJ, 658, 337 [NASA ADS] [CrossRef] [Google Scholar]
  6. Belokurov, V., Erkal, D., Evans, N. W., Koposov, S. E., & Deason, A. J. 2018, MNRAS, 478, 611 [NASA ADS] [CrossRef] [Google Scholar]
  7. Belokurov, V., Zucker, D., Evans, N., et al. 2006, ApJ, 642, L137 [NASA ADS] [CrossRef] [Google Scholar]
  8. Bengio, Y. 2011, Proceedings of the 2011 International Conference on Unsupervised and Transfer Learning Workshop in - Volume 27, UTLW’11 (JMLR.org), 17 [Google Scholar]
  9. Bengio, Y., Bastien, F., Bergeron, A., et al. 2011, in Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, eds. G. Gordon, D. Dunson, & M. Dudík (Fort Lauderdale, FL, USA: PMLR), Proc. Mach. Learn. Res., 15, 164 [Google Scholar]
  10. Bienayme, O., Robin, A. C., & Creze, M. 1987, A&A, 180, A94 [NASA ADS] [Google Scholar]
  11. Bland-Hawthorn, J., & Gerhard, O. 2016, ARA&A, 54, 529 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  12. Bonaca, A., Conroy, C., Wetzel, A., Hopkins, P. F., & Kereš, D. 2017, ApJ, 845, 101 [NASA ADS] [CrossRef] [Google Scholar]
  13. Borsato, N. W., Martell, S. L., & Simpson, J. D. 2019, MNRAS, 492, 1370 [NASA ADS] [CrossRef] [Google Scholar]
  14. Bullock, J. S., & Johnston, K. V. 2005, ApJ, 635, 931 [NASA ADS] [CrossRef] [Google Scholar]
  15. Bullock, J. S., Kravtsov, A. V., & Weinberg, D. H. 2001, ApJ, 548, 33 [NASA ADS] [CrossRef] [Google Scholar]
  16. Carleo, G., Cirac, I., Cranmer, K., et al. 2019, Rev. Mod. Phys., 91, 045002 [NASA ADS] [CrossRef] [Google Scholar]
  17. Caruana, R. 1994, Proceedings of the 7th International Conference on Neural Information Processing Systems, NIPS’94 (Cambridge, MA, USA: MIT Press), 657 [Google Scholar]
  18. Chang, S., Cohen, T., & Ostdiek, B. 2018, Phys. Rev. D, 97, 056009 [NASA ADS] [CrossRef] [Google Scholar]
  19. Chollet, F., et al. 2015, Keras. https://keras.io [Google Scholar]
  20. Chomiuk, L., & Povich, M. S. 2011, AJ, 142, 197 [NASA ADS] [CrossRef] [Google Scholar]
  21. Deng, L.-C., Newberg, H. J., Liu, C., et al. 2012, Res. Astron. Astrophys., 12, 735 [NASA ADS] [CrossRef] [Google Scholar]
  22. DESI Collaboration (Aghamousa, A., et al.) 2016, ArXiv e-prints [arXiv:1611.00036] [Google Scholar]
  23. Domínguez Sánchez, H., Huertas-Company, M., Bernardi, M., et al. 2019, MNRAS, 484, 93 [NASA ADS] [CrossRef] [Google Scholar]
  24. Donahue, J., Jia, Y., Vinyals, O., et al. 2013, ArXiv e-prints [arXiv:1310.1531] [Google Scholar]
  25. Eggen, O. J., Lynden-Bell, D., & Sandage, A. R. 1962, ApJ, 136, 748 [NASA ADS] [CrossRef] [Google Scholar]
  26. Fattahi, A., Navarro, J. F., Sawala, T., et al. 2016, MNRAS, 457, 844 [NASA ADS] [CrossRef] [Google Scholar]
  27. Faucher-Giguère, C.-A., Lidz, A., Zaldarriaga, M., & Hernquist, L. 2009, ApJ, 703, 1416 [NASA ADS] [CrossRef] [Google Scholar]
  28. Fernández-Trincado, J. G., Beers, T. C., Tang, B., et al. 2019a, MNRAS, 488, 2864 [NASA ADS] [Google Scholar]
  29. Fernández-Trincado, J. G., Moreno, E., & Pérez-Villegas, A. 2019b, MNRAS, submitted, [arXiv:1904.05370] [Google Scholar]
  30. Gaia Collaboration (Brown, A. G. A., et al.) 2018a, A&A, 616, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  31. Gaia Collaoration (Babusiaux, C., et al.) 2018b, A&A, 616, A10 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  32. Garrison-Kimmel, S., Wetzel, A., Bullock, J. S., et al. 2017, MNRAS, 471, 1709 [NASA ADS] [CrossRef] [Google Scholar]
  33. Garrison-Kimmel, S., Hopkins, P. F., Wetzel, A., et al. 2018, MNRAS, 481, 4133 [NASA ADS] [CrossRef] [Google Scholar]
  34. Glorot, X., & Bengio, Y. 2010, Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 249 [Google Scholar]
  35. Górski, K. M., Hivon, E., Banday, A. J., et al. 2005, ApJ, 622, 759 [NASA ADS] [CrossRef] [Google Scholar]
  36. Grand, R. J. J., Gómez, F. A., Marinacci, F., et al. 2017, MNRAS, 467, 179 [NASA ADS] [Google Scholar]
  37. Grand, R. J. J., Helly, J., Fattahi, A., et al. 2018, MNRAS, 481, 1726 [NASA ADS] [CrossRef] [Google Scholar]
  38. Helmi, A., & White, S. D. M. 1999, MNRAS, 307, 495 [NASA ADS] [CrossRef] [Google Scholar]
  39. Helmi, A., Veljanoski, J., Breddels, M. A., Tian, H., & Sales, L. V. 2017, A&A, 598, A58 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  40. Helmi, A., Babusiaux, C., Koppelman, H. H., et al. 2018, Nature, 563, 85 [NASA ADS] [CrossRef] [Google Scholar]
  41. Herzog-Arbeitman, J., Lisanti, M., Madau, P., & Necib, L. 2018, Phys. Rev. Lett., 120, 041102 [NASA ADS] [CrossRef] [Google Scholar]
  42. Hezaveh, Y. D., Perreault Levasseur, L., & Marshall, P. J. 2017, Nature, 548, 555 [NASA ADS] [CrossRef] [Google Scholar]
  43. Hopkins, P. F. 2015, MNRAS, 450, 53 [NASA ADS] [CrossRef] [Google Scholar]
  44. Hopkins, P. F., Narayanan, D., & Murray, N. 2013, MNRAS, 4, 432 [Google Scholar]
  45. Hopkins, P. F., Wetzel, A., Kereš, D., et al. 2018a, MNRAS, 480, 800 [NASA ADS] [CrossRef] [Google Scholar]
  46. Hopkins, P. F., Wetzel, A., Kereš, D., et al. 2018b, MNRAS, 477, 1578 [NASA ADS] [CrossRef] [Google Scholar]
  47. Hopkins, P. F., Grudić, M. Y., Wetzel, A., et al. 2020, MNRAS, 491, 3702 [NASA ADS] [Google Scholar]
  48. Huertas-Company, M., Primack, J. R., Dekel, A., et al. 2018, ApJ, 858, 114 [NASA ADS] [CrossRef] [Google Scholar]
  49. Hunter, J. D. 2007, Comput. Sci. Eng., 9, 90 [Google Scholar]
  50. Ibata, R. A., Gilmore, G., & Irwin, M. J. 1994, Nature, 370, 194 [NASA ADS] [CrossRef] [Google Scholar]
  51. Jiang, I.-G., & Binney, J. 1999, MNRAS, 303, L7 [NASA ADS] [CrossRef] [Google Scholar]
  52. Jones, E., Oliphant, T., Peterson, P., et al. 2001, SciPy: Open source scientific tools for Python [Online; Accessed 2019–04-19] [Google Scholar]
  53. Kelley, T., Bullock, J. S., Garrison-Kimmel, S., et al. 2019, MNRAS, 487, 4409 [NASA ADS] [CrossRef] [Google Scholar]
  54. Kingma, D. P., & Ba, J. 2015, 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7–9, 2015, Conference Track Proceedings [Google Scholar]
  55. Kollmeier, J. A., Zasowski, G., Rix, H. W., et al. 2017, ArXiv e-prints [arXiv:1711.03234] [Google Scholar]
  56. Koppelman, H., Helmi, A., & Veljanoski, J. 2018, ApJ, 860, L11 [NASA ADS] [CrossRef] [Google Scholar]
  57. Kroupa, P. 2001, MNRAS, 322, 231 [NASA ADS] [CrossRef] [Google Scholar]
  58. Krumholz, M. R., & Gnedin, N. Y. 2011, ApJ, 729, 36 [NASA ADS] [CrossRef] [Google Scholar]
  59. Kunder, A., Kordopatis, G., Steinmetz, M., et al. 2017, AJ, 153, 75 [NASA ADS] [CrossRef] [Google Scholar]
  60. Lancaster, L., Koposov, S. E., Belokurov, V., Evans, N. W., & Deason, A. J. 2019, MNRAS, 486, 378 [NASA ADS] [CrossRef] [Google Scholar]
  61. Larkoski, A. J., Moult, I., & Nachman, B. 2020, Phys. Rept., 841, 1 [NASA ADS] [CrossRef] [Google Scholar]
  62. Leitherer, C., Schaerer, D., Goldader, J. D., et al. 1999, ApJS, 123, 3 [NASA ADS] [CrossRef] [Google Scholar]
  63. Lowing, B., Wang, W., Cooper, A., et al. 2015, MNRAS, 446, 2274 [NASA ADS] [CrossRef] [Google Scholar]
  64. Majewski, S. R., Skrutskie, M. F., Weinberg, M. D., & Ostheimer, J. C. 2003, ApJ, 599, 1082 [NASA ADS] [CrossRef] [Google Scholar]
  65. Malhan, K., & Ibata, R. A. 2018, MNRAS, 477, 4063 [NASA ADS] [CrossRef] [Google Scholar]
  66. Marinacci, F., Pakmor, R., & Springel, V. 2014, MNRAS, 437, 1750 [NASA ADS] [CrossRef] [Google Scholar]
  67. Mateu, C., Bruzual, G., Aguilar, L., et al. 2011, MNRAS, 415, 214 [NASA ADS] [CrossRef] [Google Scholar]
  68. McKinney, W. 2010, in Proceedings of the 9th Python in Science Conference, ed. S. van der Walt, 51 [Google Scholar]
  69. Myeong, G. C., Evans, N. W., Belokurov, V., Sanders, J. L., & Koposov, S. E. 2018a, ApJ, 863, L28 [NASA ADS] [CrossRef] [Google Scholar]
  70. Myeong, G. C., Evans, N. W., Belokurov, V., Amorisco, N. C., & Koposov, S. E. 2018b, MNRAS, 475, 1537 [NASA ADS] [CrossRef] [Google Scholar]
  71. Myeong, G. C., Vasiliev, E., Iorio, G., Evans, N. W., & Belokurov, V. 2019, MNRAS, 488, 1235 [NASA ADS] [CrossRef] [Google Scholar]
  72. Necib, L., Ostdiek, B., Lisanti, M., et al. 2019a, ArXiv e-prints [arXiv:1907.07681] [Google Scholar]
  73. Necib, L., Ostdiek, B., Lisanti, M., et al. 2019b, ArXiv e-prints [arXiv:1907.07190] [Google Scholar]
  74. Necib, L., Lisanti, M., Garrison-Kimmel, S., et al. 2019c, ApJ, 883, 27 [NASA ADS] [CrossRef] [Google Scholar]
  75. Nissen, P. E., & Schuster, W. J. 2010, A&A, 511, L10 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  76. Oñorbe, J., Garrison-Kimmel, S., Maller, A. H., et al. 2014, MNRAS, 437, 1894 [NASA ADS] [CrossRef] [Google Scholar]
  77. Ostdiek, B., Necib, L., Cohen, T., et al. 2019, https://doi.org/10.5281/zenodo.3354470 [Google Scholar]
  78. Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, J. Mach. Learn. Res., 12, 2825 [Google Scholar]
  79. Posti, L., Helmi, A., Veljanoski, J., & Breddels, M. A. 2018, A&A, 615, A70 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  80. Robin, A., & Creze, M. 1986, A&A, 157, 71 [NASA ADS] [Google Scholar]
  81. Sanderson, R. E., Garrison-Kimmel, S., Wetzel, A., et al. 2018, ApJ, 869, 12 [NASA ADS] [CrossRef] [Google Scholar]
  82. Sanderson, R. E., Wetzel, A., & Loebman, S. 2020, ApJS, 246, 6 [NASA ADS] [CrossRef] [Google Scholar]
  83. Searle, L., & Zinn, R. 1978, ApJ, 225, 357 [NASA ADS] [CrossRef] [Google Scholar]
  84. Sharma, S., Bland-Hawthorn, J., Johnston, K. V., & Binney, J. 2011, ApJ, 730, 3 [NASA ADS] [CrossRef] [Google Scholar]
  85. Springel, V. 2005, MNRAS, 364, 1105 [NASA ADS] [CrossRef] [Google Scholar]
  86. van der Walt, S., Colbert, S. C., & Varoquaux, G. 2011, Comput. Sci. Eng., 13, 22 [Google Scholar]
  87. Veljanoski, J., Helmi, A., Breddels, M., & Posti, L. 2019, A&A, 621, A13 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  88. Wang, L., Dutton, A. A., Stinson, G. S., et al. 2015, MNRAS, 454, 83 [NASA ADS] [CrossRef] [Google Scholar]
  89. Weinberg, M. D. 1998, MNRAS, 299, 499 [NASA ADS] [CrossRef] [Google Scholar]
  90. Wetzel, A. R., Hopkins, P. F., Kim, J.-H., et al. 2016, ApJ, 827, L23 [NASA ADS] [CrossRef] [Google Scholar]
  91. White, S. D. M., & Rees, M. J. 1978, MNRAS, 183, 341 [NASA ADS] [CrossRef] [Google Scholar]
  92. Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. 2014, Advances in Neural Information Processing Systems, eds. Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, & K. Q. Weinberger (Curran Associates, Inc.), 27, 3320 [Google Scholar]
  93. Zhang, Y., Mesaros, A., Fujita, K., et al. 2019, Nature, 570, 484 [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.