EDP Sciences logo
Subscriber Authentication Point
Search
Menu
Home All issues Volume 707 (March 2026) A&A, 707 (2026) A363 References
Open Access
Issue
A&A
Volume 707, March 2026
Article Number A363
Number of page(s) 12
Section Numerical methods and codes
DOI https://doi.org/10.1051/0004-6361/202558358
Published online 24 March 2026
  1. Aarseth, S. J., Henon, M., & Wielen, R. 1974, A&A, 37, 183 [NASA ADS] [Google Scholar]
  2. Alvey, J., Gerdes, M., & Weniger, C. 2023, MNRAS, 525, 3662 [Google Scholar]
  3. Binney, J., & Tremaine, S. 2008, Galactic Dynamics, 2nd edn. (Princeton University Press) [Google Scholar]
  4. Bovy, J. 2014, ApJ, 795, 95 [NASA ADS] [CrossRef] [Google Scholar]
  5. Bovy, J., Bahmanyar, A., Fritz, T. K., & Kallivayalil, N. 2016, ApJ, 833, 31 [NASA ADS] [CrossRef] [Google Scholar]
  6. Bowden, A., Belokurov, V., & Evans, N. W. 2015, MNRAS, 449, 1391 [NASA ADS] [CrossRef] [Google Scholar]
  7. Bradbury, J., Frostig, R., Hawkins, P., et al. 2018, JAX: composable transformations of Python+NumPy programs [Google Scholar]
  8. Buck, T., Macciò, A. V., Dutton, A. A., Obreja, A., & Frings, J. 2019, MNRAS, 483, 1314 [NASA ADS] [CrossRef] [Google Scholar]
  9. Buck, T., Obreja, A., Macciò, A. V., et al. 2020, MNRAS, 491, 3461 [Google Scholar]
  10. Buck, T., Günes, B., Viterbo, G., Oliver, W. H., & Buder, S. 2025, A&A, 702, A184 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  11. Buder, S., Buck, T., Skúladóttir, Á., et al. 2025a, arXiv e-prints [arXiv:2510.11284] [Google Scholar]
  12. Buder, S., Buck, T., Skúladóttir, Á., et al. 2025b, arXiv e-prints [arXiv:2510.20233] [Google Scholar]
  13. Chen, Y., Valluri, M., Gnedin, O. Y., & Ash, N. 2025, ApJS, 276, 32 [Google Scholar]
  14. Cranmer, K., Brehmer, J., & Louppe, G. 2020, PNAS, 117, 30055 [NASA ADS] [CrossRef] [Google Scholar]
  15. Dax, M., Green, S. R., Gair, J., et al. 2025, Nature, 639, 49 [Google Scholar]
  16. Durkan, C., Bekasov, A., Murray, I., & Papamakarios, G. 2019, arXiv e-prints [arXiv:1906.04032] [Google Scholar]
  17. Fardal, M. A., Huang, S., & Weinberg, M. D. 2015, MNRAS, 452, 301 [NASA ADS] [CrossRef] [Google Scholar]
  18. Geffner, T., Papamakarios, G., & Mnih, A. 2022, arXiv e-prints [arXiv:2209.14249] [Google Scholar]
  19. Gunes, B., Buder, S., & Buck, T. 2025, arXiv e-prints [arXiv:2507.05060] [Google Scholar]
  20. Ho, J., Jain, A., & Abbeel, P. 2020, arXiv e-prints [arXiv:2006.11239] [Google Scholar]
  21. Ho, M., Bartlett, D. J., Chartier, N., et al. 2024, Open J. Astrophys., 7, 54 [NASA ADS] [CrossRef] [Google Scholar]
  22. Holzschuh, B., & Thuerey, N. 2024, arXiv e-prints [arXiv:2410.22573] [Google Scholar]
  23. Ibata, R., Malhan, K., Tenachi, W., et al. 2024, ApJ, 967, 89 [NASA ADS] [CrossRef] [Google Scholar]
  24. Kidger, P. 2022, arXiv e-prints [arXiv:2202.02435] [Google Scholar]
  25. Koposov, S. E., Rix, H.-W., & Hogg, D. W. 2010, ApJ, 712, 260 [Google Scholar]
  26. Küpper, A. H. W., Balbinot, E., Bonaca, A., et al. 2015, ApJ, 803, 80 [Google Scholar]
  27. Lee, J., Lee, Y., Kim, J., et al. 2018, arXiv e-prints [arXiv:1810.00825] [Google Scholar]
  28. Lemos, P., Coogan, A., Hezaveh, Y., & Perreault-Levasseur, L. 2023, 40th International Conference on Machine Learning, 202, 19256 [Google Scholar]
  29. Lipman, Y., Chen, R. T. Q., Ben-Hamu, H., Nickel, M., & Le, M. 2022, arXiv e-prints [arXiv:2210.02747] [Google Scholar]
  30. Malhan, K., & Ibata, R. A. 2019, MNRAS, 486, 2995 [NASA ADS] [CrossRef] [Google Scholar]
  31. Mestre, M. F., Argüelles, C. R., Carpintero, D. D., Crespi, V., & Krut, A. 2024, A&A, 689, A194 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  32. Miró-Carretero, J., Gómez-Flechoso, M. A., Martínez-Delgado, D., et al. 2025, A&A, 700, A176 [Google Scholar]
  33. Nguyen, T., Mishra-Sharma, S., Williams, R., & Necib, L. 2023, Phys. Rev. D, 107, 043015 [Google Scholar]
  34. Nibauer, J., & Bonaca, A. 2025, ApJ, 985, L22 [Google Scholar]
  35. Nibauer, J., Belokurov, V., Cranmer, M., Goodman, J., & Ho, S. 2022, ApJ, 940, 22 [NASA ADS] [CrossRef] [Google Scholar]
  36. Nibauer, J., Bonaca, A., Spergel, D. N., et al. 2025, ApJ, 983, 68 [Google Scholar]
  37. Obreja, A., Buck, T., & Macciò, A. V. 2022, A&A, 657, A15 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  38. Oliver, W. H., & Buck, T. 2024, arXiv e-prints [arXiv:2411.03229] [Google Scholar]
  39. Oliver, W. H., Elahi, P. J., Lewis, G. F., & Buck, T. 2024, MNRAS, 530, 2637 [Google Scholar]
  40. Palau, C. G., Wang, W., & Han, J. 2025, MNRAS, 539, 2718 [Google Scholar]
  41. Perez, E., Strub, F., de Vries, H., Dumoulin, V., & Courville, A. 2017, arXiv e-prints [arXiv:1709.07871] [Google Scholar]
  42. Price-Whelan, A. M., Hogg, D. W., Johnston, K. V., & Hendel, D. 2014, ApJ, 794, 4 [NASA ADS] [CrossRef] [Google Scholar]
  43. Reino, S., Rossi, E. M., Sanderson, R. E., et al. 2022, MNRAS, 512, 4455 [Google Scholar]
  44. Sante, A., Kawata, D., Font, A. S., & Grand, R. J. J. 2025, MNRAS, 542, 1776 [Google Scholar]
  45. Saoulis, A. A., Piras, D., Jeffrey, N., et al. 2025, MNRAS [arXiv:2505.21215] [Google Scholar]
  46. Schmitt, M., Bürkner, P.-C., Köthe, U., & Radev, S. T. 2021, arXiv e-prints [arXiv:2112.08866] [Google Scholar]
  47. Skúladóttir, Á., Ernandes, H., Feuillet, D. K., et al. 2025, ApJ, 986, L21 [Google Scholar]
  48. Starkman, N., Price-Whelan, A., & Nibauer, J. 2024, https://doi.org/10.5281/zenodo.11553485 [Google Scholar]
  49. Viterbo, G., & Buck, T. 2024, arXiv e-prints [arXiv:2411.17269] [Google Scholar]
  50. Viterbo, G., & Buck, T. 2025, arXiv e-prints [arXiv:2511.22468] [Google Scholar]
  51. Walder, M., Erkal, D., Collins, M., & Martínez-Delgado, D. 2025, ApJ, 994, 36 [Google Scholar]
  52. Wildberger, J. B., Dax, M., Buchholz, S., et al. 2023, Machine Learning for Astrophysics, 34 [Google Scholar]
  53. 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.