EDP Sciences logo
Subscriber Authentication Point
Search
Menu
Home All issues Volume 702 (October 2025) A&A, 702 (2025) A184 References
Open Access
Issue
A&A
Volume 702, October 2025
Article Number A184
Number of page(s) 15
Section Galactic structure, stellar clusters and populations
DOI https://doi.org/10.1051/0004-6361/202554306
Published online 17 October 2025
  1. Abdurro’uf, Accetta, K., Aerts, C., et al. 2022, ApJS, 259, 35 [NASA ADS] [CrossRef] [Google Scholar]
  2. Agertz, O., Renaud, F., Feltzing, S., et al. 2021, MNRAS, 503, 5826 [NASA ADS] [CrossRef] [Google Scholar]
  3. Andrews, B. H., Weinberg, D. H., Schönrich, R., & Johnson, J. A. 2017, ApJ, 835, 224 [NASA ADS] [CrossRef] [Google Scholar]
  4. Asplund, M., Grevesse, N., Sauval, A. J., & Scott, P. 2009, ARA&A, 47, 481 [NASA ADS] [CrossRef] [Google Scholar]
  5. Astropy Collaboration (Robitaille, T. P., et al.) 2013, A&A, 558, A33 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  6. Astropy Collaboration (Price-Whelan, A. M., et al.) 2018, AJ, 156, 123 [Google Scholar]
  7. Bigiel, F., Leroy, A., Walter, F., et al. 2008, AJ, 136, 2846 [NASA ADS] [CrossRef] [Google Scholar]
  8. Buck, T. 2020, MNRAS, 491, 5435 [NASA ADS] [CrossRef] [Google Scholar]
  9. Buck, T., Obreja, A., Macciò, A. V., et al. 2020, MNRAS, 491, 3461 [Google Scholar]
  10. Buck, T., Rybizki, J., Buder, S., et al. 2021, MNRAS, 508, 3365 [NASA ADS] [CrossRef] [Google Scholar]
  11. Buck, T., Obreja, A., Ratcliffe, B., et al. 2023, MNRAS, 523, 1565 [CrossRef] [Google Scholar]
  12. Buder, S., Sharma, S., Kos, J., et al. 2021, MNRAS, 506, 150 [NASA ADS] [CrossRef] [Google Scholar]
  13. Buder, S., Kos, J., Wang, X. E., et al. 2025, PASA, 42, e051 [Google Scholar]
  14. Chabrier, G. 2003, PASP, 115, 763 [Google Scholar]
  15. Chabrier, G., Hennebelle, P., & Charlot, S. 2014, ApJ, 796, 75 [Google Scholar]
  16. Clarke, A. J., Debattista, V. P., Nidever, D. L., et al. 2019, MNRAS, 484, 3476 [NASA ADS] [CrossRef] [Google Scholar]
  17. Clauwens, B., Schaye, J., & Franx, M. 2016, MNRAS, 462, 2832 [Google Scholar]
  18. Cook, S. R., Gelman, A., & Rubin, D. B. 2006, J. Computat. Graph. Statist., 15, 675 [Google Scholar]
  19. Côté, B., & Ritter, C. 2018, OMEGA: One-zone Model for the Evolution of GAlaxies, Astrophysics Source Code Library [record ascl:1806.018] [Google Scholar]
  20. Côté, B., Ritter, C., O’Shea, B. W., et al. 2016, ApJ, 824, 82 [CrossRef] [Google Scholar]
  21. Cranmer, K., Brehmer, J., & Louppe, G. 2020, PNAS, 117, 30055 [NASA ADS] [CrossRef] [Google Scholar]
  22. Dalton, G., Trager, S., Abrams, D. C., et al. 2018, SPIE Conf. Ser., 10702, 107021B [Google Scholar]
  23. de Jong, R. S., Barden, S., Bellido-Tirado, O., et al. 2014, SPIE Conf. Ser., 9147, 91470M [NASA ADS] [Google Scholar]
  24. Defazio, A., Yang, X. A., Mehta, H., et al. 2024, arXiv e-prints, [arXiv:2405.15682] [Google Scholar]
  25. Doherty, C. L., Gil-Pons, P., Lau, H. H. B., Lattanzio, J. C., & Siess, L. 2014, MNRAS, 437, 195 [Google Scholar]
  26. Durkan, C., Bekasov, A., Murray, I., & Papamakarios, G. 2019, Neural Spline Flows [Google Scholar]
  27. Fishlock, C. K., Karakas, A. I., Lugaro, M., & Yong, D. 2014, ApJ, 797, 44 [Google Scholar]
  28. Font, A. S., McCarthy, I. G., Poole-Mckenzie, R., et al. 2020, MNRAS, 498, 1765 [NASA ADS] [CrossRef] [Google Scholar]
  29. Geffner, T., Papamakarios, G., & Mnih, A. 2022, arXiv e-prints [arXiv:2209.14249] [Google Scholar]
  30. Gloeckler, M., Deistler, M., Weilbach, C., Wood, F., & Macke, J. H. 2024, arXiv, [arXiv:2404.09636] [Google Scholar]
  31. Grand, R. J. J., Bustamante, S., Gómez, F. A., et al. 2018, MNRAS, 474, 3629 [NASA ADS] [CrossRef] [Google Scholar]
  32. Griffith, E., Johnson, J. A., & Weinberg, D. H. 2019, ApJ, 886, 84 [Google Scholar]
  33. Gunes, B., Buder, S., & Buck, T. 2025, arXiv e-prints [arXiv:2507.05060] [Google Scholar]
  34. Gutcke, T. A., & Springel, V. 2019, MNRAS, 482, 118 [NASA ADS] [CrossRef] [Google Scholar]
  35. Ho, M., Bartlett, D. J., Chartier, N., et al. 2024, Open J. Astrophys., 7, 54 [NASA ADS] [CrossRef] [Google Scholar]
  36. Hopkins, P. F., Wetzel, A., Kereš, D., et al. 2018, MNRAS, 480, 800 [NASA ADS] [CrossRef] [Google Scholar]
  37. Jiménez, N., Tissera, P. B., & Matteucci, F. 2015, ApJ, 810, 137 [Google Scholar]
  38. Johnson, J. W., & Weinberg, D. H. 2020, MNRAS, 498, 1364 [CrossRef] [Google Scholar]
  39. Karakas, A. I. 2010, MNRAS, 403, 1413 [NASA ADS] [CrossRef] [Google Scholar]
  40. Karakas, A. I., & Lugaro, M. 2016, ApJ, 825, 26 [NASA ADS] [CrossRef] [Google Scholar]
  41. Kobayashi, C., Umeda, H., Nomoto, K., Tominaga, N., & Ohkubo, T. 2006, ApJ, 653, 1145 [NASA ADS] [CrossRef] [Google Scholar]
  42. Lemos, P., Coogan, A., Hezaveh, Y., & Perreault-Levasseur, L. 2023, 40th International Conference on Machine Learning, 202, 19256 [Google Scholar]
  43. Luger, R., Bedell, M., Foreman-Mackey, D., et al. 2021, arXiv e-prints [arXiv:2110.06271] [Google Scholar]
  44. Mackereth, J. T., Crain, R. A., Schiavon, R. P., et al. 2018, MNRAS, 477, 5072 [NASA ADS] [CrossRef] [Google Scholar]
  45. Majewski, S. R., APOGEE Team, & APOGEE-2 Team 2016, xAstron. Nachr., 337, 863 [Google Scholar]
  46. Maoz, D., & Mannucci, F. 2012, PASA, 29, 447 [NASA ADS] [CrossRef] [Google Scholar]
  47. Maoz, D., Sharon, K., & Gal-Yam, A. 2010, ApJ, 722, 1879 [Google Scholar]
  48. Maoz, D., Mannucci, F., & Brandt, T. D. 2012, MNRAS, 426, 3282 [NASA ADS] [CrossRef] [Google Scholar]
  49. Martín-Navarro, I., Lyubenova, M., van de Ven, G., et al. 2019, A&A, 626, A124 [Google Scholar]
  50. Minchev, I., Chiappini, C., & Martig, M. 2013, A&A, 558, A9 [CrossRef] [EDP Sciences] [Google Scholar]
  51. Mollá, M., Cavichia, O., Gavilán, M., & Gibson, B. K. 2015, MNRAS, 451, 3693 [CrossRef] [Google Scholar]
  52. Nelson, D., Springel, V., Pillepich, A., et al. 2019, Computat. Astrophys. Cosmol., 6, 2 [NASA ADS] [CrossRef] [Google Scholar]
  53. Ness, M. K., Johnston, K. V., Blancato, K., et al. 2019, ApJ, 883, 177 [CrossRef] [Google Scholar]
  54. Nomoto, K., Iwamoto, K., Nakasato, N., et al. 1997, Nucl. Phys. A, 621, 467 [NASA ADS] [CrossRef] [Google Scholar]
  55. Nomoto, K., Kobayashi, C., & Tominaga, N. 2013, ARA&A, 51, 457 [CrossRef] [Google Scholar]
  56. Papamakarios, G., Pavlakou, T., & Murray, I. 2018, Masked Autoregressive Flow for Density Estimation [Google Scholar]
  57. Papamakarios, G., Nalisnick, E., Rezende, D. J., Mohamed, S., & Lakshminarayanan, B. 2021, Normalizing Flows for Probabilistic Modeling and Inference [Google Scholar]
  58. Philcox, O., Rybizki, J., & Gutcke, T. A. 2018, ApJ, 861, 40 [Google Scholar]
  59. Philcox, O. H. E., & Rybizki, J. 2019, ApJ, 887, 9 [Google Scholar]
  60. Philcox, O., Rybizki, J., & Gutcke, T. A. 2018, ApJ, 861, 40 [Google Scholar]
  61. Pillepich, A., Springel, V., Nelson, D., et al. 2018, mnras, 473, 4077 [Google Scholar]
  62. Planck Collaboration XIII 2016, A&A, 594, A13 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  63. Portinari, L., Chiosi, C., & Bressan, A. 1998, A&A, 334, 505 [NASA ADS] [Google Scholar]
  64. Romano, D., Chiappini, C., Matteucci, F., & Tosi, M. 2005, A&A, 430, 491 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  65. Rybizki, J., & Just, A. 2015, MNRAS, 447, 3880 [NASA ADS] [CrossRef] [Google Scholar]
  66. Rybizki, J., Just, A., & Rix, H.-W. 2017, A&A, 605, A59 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  67. Sawala, T., Frenk, C. S., Fattahi, A., et al. 2016, MNRAS, 457, 1931 [Google Scholar]
  68. Schönrich, R., & Binney, J. 2009, MNRAS, 396, 203 [Google Scholar]
  69. Spurio Mancini, A., Docherty, M. M., Price, M. A., & McEwen, J. D. 2023, RAS Tech. Instrum., 2, 710 [Google Scholar]
  70. Talbot, R. J., Jr., & Arnett, W. D. 1971, ApJ, 170, 409 [NASA ADS] [CrossRef] [Google Scholar]
  71. Talts, S., Betancourt, M., Simpson, D., Vehtari, A., & Gelman, A. 2018, arXiv e-prints [arXiv:1804.06788] [Google Scholar]
  72. Thielemann, F. K., Argast, D., Brachwitz, F., et al. 2003, Nucl. Phys. A, 718, 139 [NASA ADS] [CrossRef] [Google Scholar]
  73. van Dokkum, P. G., Leja, J., Nelson, E. J., et al. 2013, ApJ, 771, L35 [NASA ADS] [CrossRef] [Google Scholar]
  74. Vincenzo, F., Matteucci, F., Recchi, S., et al. 2015, MNRAS, 449, 1327 [NASA ADS] [CrossRef] [Google Scholar]
  75. Viterbo, G., & Buck, T. 2024, arXiv e-prints [arXiv:2411.17269] [Google Scholar]
  76. Wang, L., Dutton, A. A., Stinson, G. S., et al. 2015, MNRAS, 454, 83 [NASA ADS] [CrossRef] [Google Scholar]
  77. Wang, K., Carrillo, A., Ness, M. K., & Buck, T. 2024, MNRAS, 527, 321 [Google Scholar]
  78. Weinberg, D. H., Holtzman, J. A., Hasselquist, S., et al. 2019, ApJ, 874, 102 [NASA ADS] [CrossRef] [Google Scholar]
  79. Weisz, D. R., Johnson, L. C., Foreman-Mackey, D., et al. 2015, ApJ, 806, 198 [NASA ADS] [CrossRef] [Google Scholar]
  80. Zeghal, J., Lanusse, F., Boucaud, A., Remy, B., & Aubourg, E. 2022, Neural Posterior Estimation with Differentiable Simulators [Google Scholar]
  81. Zhou, L., Radev, S. T., Oliver, W. H., et al. 2025, A&A, 701, A44 [NASA ADS] [CrossRef] [EDP Sciences] [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.