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Home All issues Volume 706 (February 2026) A&A, 706 (2026) A201 References
Open Access
Issue
A&A
Volume 706, February 2026
Article Number A201
Number of page(s) 17
Section Galactic structure, stellar clusters and populations
DOI https://doi.org/10.1051/0004-6361/202556710
Published online 13 February 2026
  1. Anand, G. S., Lee, J. C., Van Dyk, S. D., et al. 2021, MNRAS, 501, 3621 [Google Scholar]
  2. Ardizzone, L., Bungert, T., Draxler, F., et al. 2018 and 2022, Framework for Easily Invertible Architectures (FrEIA) [Google Scholar]
  3. Ardizzone, L., Lüth, C., Kruse, J., Rother, C., & Köthe, U. 2019, arXiv e-prints [arXiv:1907.02392] [Google Scholar]
  4. Bister, T., Erdmann, M., Köthe, U., & Schulte, J. 2023, J. Phys.: Conf. Ser., 2438, 012094 [Google Scholar]
  5. Boquien, M., Burgarella, D., Roehlly, Y., et al. 2019, A&A, 622, A103 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  6. Bruzual, G., & Charlot, S. 2003, MNRAS, 344, 1000 [NASA ADS] [CrossRef] [Google Scholar]
  7. Burgarella, D., Buat, V., & Iglesias-Páramo, J. 2005, MNRAS, 360, 1413 [NASA ADS] [CrossRef] [Google Scholar]
  8. Calzetti, D., Lee, J. C., Sabbi, E., et al. 2015, AJ, 149, 51 [Google Scholar]
  9. Candebat, N., Sacco, G. G., Magrini, L., et al. 2024, A&A, 692, A228 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  10. Cardelli, J. A., Clayton, G. C., & Mathis, J. S. 1989, ApJ, 345, 245 [Google Scholar]
  11. Chandar, R., Whitmore, B. C., Dinino, D., et al. 2016, ApJ, 824, 71 [NASA ADS] [CrossRef] [Google Scholar]
  12. Cranmer, K., Brehmer, J., & Louppe, G. 2020, PNAS, 117, 30055 [NASA ADS] [CrossRef] [Google Scholar]
  13. da Silva, R. L., Fumagalli, M., & Krumholz, M. 2012, ApJ, 745, 145 [CrossRef] [Google Scholar]
  14. Denker, A., Schmidt, M., Leuschner, J., & Maass, P. 2021, J. Imaging, 7. 243 [Google Scholar]
  15. Dinh, L., Sohl-Dickstein, J., & Bengio, S. 2017, in International Conference on Learning Representations [Google Scholar]
  16. Eisert, L., Pillepich, A., Nelson, D., et al. 2023, MNRAS, 519, 2199 [Google Scholar]
  17. Fall, S. M., Chandar, R., & Whitmore, B. C. 2005, ApJ, 631, L133 [NASA ADS] [CrossRef] [Google Scholar]
  18. Fukunaga, K., & Hostetler, L. 1975, IEEE Trans. Inform. Theory, 21, 32 [CrossRef] [Google Scholar]
  19. Gelman, A., Meng, X.-L., & Stern, H. 1996, Statist. Sin., 6, 733 [Google Scholar]
  20. Gelman, A., Carlin, J. B., Stern, H. S., et al. 2013, Bayesian Data Analysis, 3rd edn., Chapman & Hall/CRC Texts in Statistical Science Series (CRC) [Google Scholar]
  21. Gontcharov, G. A., Mosenkov, A. V., & Khovritchev, M. Y. 2019, MNRAS, 483, 4949 [NASA ADS] [CrossRef] [Google Scholar]
  22. Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., et al. 2014, in Advances in Neural Information Processing Systems, 27, eds. Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, & K. Weinberger (Curran Associates, Inc.) [Google Scholar]
  23. Hahn, C., & Melchior, P. 2022, ApJ, 938, 11 [NASA ADS] [CrossRef] [Google Scholar]
  24. Haldemann, J., Ksoll, V., Walter, D., et al. 2023, A&A, 672, A180 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  25. Hermans, J., Begy, V., & Louppe, G. 2020, in Proceedings of Machine Learning Research, 119, Proceedings of the 37th International Conference on Machine Learning, eds. H. D. III, & A. Singh (PMLR), 4239 [Google Scholar]
  26. Himes, M. D., Harrington, J., Cobb, A. D., et al. 2022, Planet. Sci. J., 3, 91 [NASA ADS] [CrossRef] [Google Scholar]
  27. Kang, D. E., Pellegrini, E. W., Ardizzone, L., et al. 2022, MNRAS, 512, 617 [CrossRef] [Google Scholar]
  28. Kang, D. E., Klessen, R. S., Ksoll, V. F., et al. 2023, MNRAS, 520, 4981 [NASA ADS] [CrossRef] [Google Scholar]
  29. Kang, D. E., Ksoll, V. F., Itrich, D., et al. 2023, A&A, 674, A175 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  30. Khullar, G., Nord, B., Ćiprijanović, A., Poh, J., & Xu, F. 2022, Mach. Learn.: Sci. Technol., 3, 04LT04 [Google Scholar]
  31. Kingma, D. P., & Welling, M. 2013, arXiv e-prints [arXiv:1312.6114] [Google Scholar]
  32. Kingma, D. P., & Ba, J. 2014, arXiv e-prints [arXiv:1412.6980] [Google Scholar]
  33. Kobyzev, I., Prince, S. J., & Brubaker, M. A. 2021, IEEE Trans. Pattern Anal. Mach. Intell., 43, 3964 [NASA ADS] [CrossRef] [Google Scholar]
  34. Ksoll, V. F., Reissl, S., Klessen, R. S., et al. 2024, A&A, 683, A246 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  35. Ksoll, V. F., Ardizzone, L., Klessen, R., et al. 2020, MNRAS, 499, 5447 [NASA ADS] [CrossRef] [Google Scholar]
  36. Lee, J. C., Whitmore, B. C., Thilker, D. A., et al. 2022, ApJS, 258, 10 [NASA ADS] [CrossRef] [Google Scholar]
  37. Lee, J. C., Sandstrom, K. M., Leroy, A. K., et al. 2023, ApJ, 944, L17 [NASA ADS] [CrossRef] [Google Scholar]
  38. Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., & Talwalkar, A. 2018, J. Mach. Learn. Res., 18, 1 [Google Scholar]
  39. Maschmann, D., Lee, J. C., Thilker, D. A., et al. 2024, ApJS, 273, 14 [Google Scholar]
  40. Nölke, J.-H., Adler, T., Gröhl, J., et al. 2021, in Bildverarbeitung für die Medizin 2021, eds. C. Palm, T. M. Deserno, H. Handels, A. Maier, K. Maier-Hein, & T. Tolxdorff (Wiesbaden: Springer Fachmedien Wiesbaden), 330 [Google Scholar]
  41. Noll, S., Burgarella, D., Giovannoli, E., et al. 2009, A&A, 507, 1793 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  42. Orozco-Duarte, R., Wofford, A., Vidal-García, A., et al. 2022, MNRAS, 509, 522 [Google Scholar]
  43. Papamakarios, G., Pavlakou, T., & Murray, I. 2017, in Advances in Neural Information Processing Systems, 30, eds. I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Curran Associates, Inc.) [Google Scholar]
  44. Papamakarios, G., Nalisnick, E., Rezende, D. J., Mohamed, S., & Lakshminarayanan, B. 2021, J. Mach. Learn. Res., 22, 1 [Google Scholar]
  45. Paszke, A., Gross, S., Massa, F., et al. 2019, in Advances in Neural Information Processing Systems, 32, eds. H. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché-Buc, E. Fox, & R. Garnett (Curran Associates, Inc.) [Google Scholar]
  46. Rubin, D. B. 1984, Ann. Statist., 12, 1151 [CrossRef] [Google Scholar]
  47. Serra, P., Amblard, A., Temi, P., et al. 2011, ApJ, 740, 22 [Google Scholar]
  48. Talts, S., Betancourt, M., Simpson, D., Vehtari, A., & Gelman, A. 2018, arXiv e-prints [arXiv:1804.06788] [Google Scholar]
  49. Thilker, D. A., Whitmore, B. C., Lee, J. C., et al. 2022, MNRAS, 509, 4094 [Google Scholar]
  50. Turner, J. A., Dale, D. A., Lee, J. C., et al. 2021, MNRAS, 502, 1366 [NASA ADS] [CrossRef] [Google Scholar]
  51. Vasist, M., Rozet, F., Absil, O., et al. 2023, A&A, 672, A147 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  52. Walter, D. 2023, Inferring Properties of Star Clusters from Unresolved Broadband Photometry with a Flow-Based Generative Model, master’s thesis, unpublished [Google Scholar]
  53. Zhang, K., Jayasinghe, T., & Bloom, J. 2023a, in Machine Learning for Astrophysics, 39 [Google Scholar]
  54. Zhang, R., Yuan, H., & Chen, B. 2023b, ApJS, 269, 6 [NASA ADS] [CrossRef] [Google Scholar]

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