Open Access
Issue
A&A
Volume 704, December 2025
Article Number A279
Number of page(s) 16
Section Stellar atmospheres
DOI https://doi.org/10.1051/0004-6361/202555376
Published online 22 December 2025
  1. Alsing, J., Peiris, H., Leja, J., et al. 2020, ApJS, 249, 5 [CrossRef] [Google Scholar]
  2. Angiulli, F., Fassetti, F., & Ferragina, L., 2023, arXiv e-prints [arXiv:2305.10464] [Google Scholar]
  3. Aquilué-Llorens, D. & Soria-Frisch, A., 2025, arXiv e-prints [arXiv:2502.08686] [Google Scholar]
  4. Aoki, W., Beers, T. C., Christlieb, N., et al. 2007, ApJ, 655, 492 [NASA ADS] [CrossRef] [Google Scholar]
  5. Ardern-Arentsen, A., Kane, S. G., Belokurov, V., et al. 2025, MNRAS, 537, 1984 [Google Scholar]
  6. Arentsen, A., Starkenburg, E., Shetrone, M. D., et al. 2019, A&A, 621, A108 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  7. Asplund, M., 2001, in ASPCS, 223, 11th Cambridge Workshop on Cool Stars, Stellar Systems and the Sun, eds. R. J. Garcia Lopez, R. Rebolo, & M. R. Zapaterio Osorio, 217 [Google Scholar]
  8. Asplund, M., Nordlund, Å., & Trampedach, R. 1999, in ASPCS, 173, Stellar Structure: Theory and Test of Connective Energy Transport, eds. A. Gimenez, E. F. Guinan, & B. Montesinos, 221 [Google Scholar]
  9. Beers, T. C., & Christlieb, N., 2005, ARA&A, 43, 531 [NASA ADS] [CrossRef] [Google Scholar]
  10. Belokurov, V., & Kravtsov, A., 2024, MNRAS, 528, 3198 [CrossRef] [Google Scholar]
  11. Bengio, Y., Courville, A., & Vincent, P., 2013, IEEE Trans. Pattern Anal. Mach. Intell., 35, 1798 [CrossRef] [Google Scholar]
  12. Blanco-Cuaresma, S., 2019, MNRAS, 486, 2075 [Google Scholar]
  13. Blanco-Cuaresma, S., Soubiran, C., Heiter, U., & Jofré, P., 2014, A&A, 569, A111 [CrossRef] [EDP Sciences] [Google Scholar]
  14. Bu, Y., Zhao, G., Pan, J., & Bharat Kumar, Y., 2016, ApJ, 817, 78 [Google Scholar]
  15. Buck, T., & Schwarz, C., 2024, arXiv e-prints [arXiv:2410.16081] [Google Scholar]
  16. Buder, S., Sharma, S., Kos, J., et al. 2021, MNRAS, 506, 150 [NASA ADS] [CrossRef] [Google Scholar]
  17. Carbajal, G., Richter, J., & Gerkmann, T., 2021, in 2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 126 [Google Scholar]
  18. Carvalho, D. S., Mercatali, G., Zhang, Y., & Freitas, A., 2022, arXiv e-prints [arXiv:2210.02898] [Google Scholar]
  19. Chen, T., & Guestrin, C., 2016, arXiv e-prints [arXiv:1603.02754] [Google Scholar]
  20. Chiappini, C., 2001, Am. Sci., 89, 506 [Google Scholar]
  21. Csiszar, I., 1975, Ann. Probab., 3, 146 [Google Scholar]
  22. Cui, X.-Q., Zhao, Y.-H., Chu, Y.-Q., et al. 2012, Res. Astron. Astrophys., 12, 1197 [Google Scholar]
  23. Dawson, K. S., Schlegel, D. J., Ahn, C. P., et al. 2013, AJ, 145, 10 [Google Scholar]
  24. De Lucia, G., & Helmi, A., 2008, MNRAS, 391, 14 [NASA ADS] [CrossRef] [Google Scholar]
  25. de Mijolla, D., Ness, M. K., Viti, S., & Wheeler, A. J., 2021, ApJ, 913, 12 [NASA ADS] [CrossRef] [Google Scholar]
  26. De Silva, G., Freeman, K., Bland-Hawthorn, J., et al. 2015, MNRAS, 449, 2604 [NASA ADS] [CrossRef] [Google Scholar]
  27. Dittadi, A., Träuble, F., Locatello, F., et al. 2020, arXiv e-prints [arXiv:2010.14407] [Google Scholar]
  28. Dupuy, M., Chavent, M., & Dubois, R., 2024, mDAE: modified Denoising AutoEncoder for missing data imputation [Google Scholar]
  29. Eitner, P., Bergemann, M., Hoppe, R., et al. 2025, A&A, 703, A199 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  30. Emukpere, D., Deffayet, R., Wu, B., et al. 2025, Disentangled Object-Centric Image Representation for Robotic Manipulation [Google Scholar]
  31. Fang, H., Carbajal, G., Wermter, S., & Gerkmann, T., 2021, in ICASSP 2021–2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 676 [Google Scholar]
  32. Frebel, A., & Norris, J. E., 2015, ARA&A, 53, 631 [NASA ADS] [CrossRef] [Google Scholar]
  33. Frebel, A., Johnson, J. L., & Bromm, V., 2007, MNRAS, 380, L40 [NASA ADS] [CrossRef] [Google Scholar]
  34. Gilmore, G., Randich, S., Worley, C., et al. 2022, A&A, 666, A120 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  35. Goodfellow, I., Bengio, Y., & Courville, A., 2016, Deep Learning (MIT Press) [Google Scholar]
  36. Gui, J., Chen, T., Zhang, J., et al. 2023, arXiv e-prints [arXiv:2301.05712] [Google Scholar]
  37. Gustafsson, B., Edvardsson, B., Eriksson, K., et al. 2008, A&A, 486, 951 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  38. Hansen, T. T., Andersen, J., Nordström, B., et al. 2016, A&A, 586, A160 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  39. Hartwig, T., & Yoshida, N., 2019, ApJ, 870, L3 [Google Scholar]
  40. Hawkins, K., Jofré, P., Gilmore, G., & Masseron, T., 2014, MNRAS, 445, 2575 [CrossRef] [Google Scholar]
  41. He, K., Zhang, X., Ren, S., & Sun, J., 2015, in Proceedings of the IEEE International Conference on Computer Vision, 1026 [Google Scholar]
  42. Helmi, A., 2020, ARA&A, 58, 205 [Google Scholar]
  43. Ho, A. Y. Q., Ness, M. K., Hogg, D. W., et al. 2017, ApJ, 836, 5 [NASA ADS] [CrossRef] [Google Scholar]
  44. Holly, S., Heel, R., Katic, D., et al. 2022, arXiv e-prints [arXiv:2210.08011] [Google Scholar]
  45. Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K., 2006, Neurocomputing, 70, 489 [CrossRef] [Google Scholar]
  46. Hudson, M. J., Gillis, B. R., Coupon, J., et al. 2015, MNRAS, 447, 298 [Google Scholar]
  47. Jofré, P., Heiter, U., & Soubiran, C., 2019, ARA&A, 57, 571 [Google Scholar]
  48. Jofré, P., Heiter, U., Soubiran, C., et al. 2014, A&A, 564, A133 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  49. Johnson, J. W., Conroy, C., Johnson, B. D., et al. 2023, MNRAS, 526, 5084 [NASA ADS] [CrossRef] [Google Scholar]
  50. Kingma, D. P., 2014, arXiv e-prints [arXiv:1412.6980] [Google Scholar]
  51. Kingma, D. P., & Welling, M., 2013, arXiv e-prints [arXiv:1312.6114] [Google Scholar]
  52. Kollmeier, J., Anderson, S. F., Blanc, G. A., et al. 2019, in Bulletin of the American Astronomical Society, 51, 274 [Google Scholar]
  53. Lample, G., Zeghidour, N., Usunier, N., et al. 2017, arXiv e-prints [arXiv:1706.00409] [Google Scholar]
  54. Lee, Y. S., Beers, T. C., Prieto, C. A., et al. 2011, AJ, 141, 90 [NASA ADS] [CrossRef] [Google Scholar]
  55. Lee, Y. S., Beers, T. C., Masseron, T., et al. 2013, AJ, 146, 132 [Google Scholar]
  56. Leung, H. W., & Bovy, J., 2019, MNRAS, 483, 3255 [NASA ADS] [Google Scholar]
  57. Leung, H. W., Bovy, J., Mackereth, J. T., & Miglio, A., 2023, MNRAS, 522, 4577 [NASA ADS] [CrossRef] [Google Scholar]
  58. Li, H., Aoki, W., Matsuno, T., et al. 2022, ApJ, 931, 147 [NASA ADS] [CrossRef] [Google Scholar]
  59. Li, H., Wang, X., Zhang, Z., & Zhu, W., 2022, IEEE Trans. Knowl. Data Eng., 35, 7328 [Google Scholar]
  60. Li, G., Lu, Z., Wang, J., & Wang, Z., 2025, arXiv e-prints [arXiv:2502.15300] [Google Scholar]
  61. Liu, H., Du, C., Deng, M., & Zhang, J., 2025, MNRAS, 541, 58 [Google Scholar]
  62. Locatello, F., Bauer, S., Lucic, M., et al. 2018, arXiv e-prints [arXiv:1811.12359] [Google Scholar]
  63. Magic, Z., Collet, R., Asplund, M., et al. 2013, A&A, 557, A26 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  64. Magic, Z., Collet, R., & Asplund, M., 2014, arXiv e-prints [arXiv:1403.6245] [Google Scholar]
  65. Magic, Z., Weiss, A., & Asplund, M., 2015, A&A, 573, A89 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  66. Majewski, S. R., Schiavon, R. P., Frinchaboy, P. M., et al. 2017, AJ, 154 [Google Scholar]
  67. Manea, C., Hawkins, K., Ness, M. K., et al. 2024, ApJ, 972, 69 [Google Scholar]
  68. Manteiga, M., Santoveña, R., Álvarez, M. A., et al. 2025, A&A, 694, A326 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  69. Marsteller, B., Beers, T. C., Sivarani, T., et al. 2009, AJ, 138, 533 [Google Scholar]
  70. Mas-Buitrago, P., González-Marcos, A., Solano, E., et al. 2024, A&A, 687, A205 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  71. Melchior, P., Liang, Y., Hahn, C., & Goulding, A., 2023, AJ, 166, 74 [NASA ADS] [CrossRef] [Google Scholar]
  72. Mu, J., Qiu, W., Kortylewski, A., et al. 2021, in Proceedings of the IEEE/CVF International Conference on Computer Vision, 13001 [Google Scholar]
  73. Ness, M., Hogg, D. W., Rix, H. W., Ho, A. Y. Q., & Zasowski, G., 2015, ApJ, 808, 16 [NASA ADS] [CrossRef] [Google Scholar]
  74. Nissen, P. E., & Schuster, W. J., 1997, A&A, 326, 751 [NASA ADS] [Google Scholar]
  75. Norris, J. E., Yong, D., Bessell, M. S., et al. 2013, ApJ, 762, 28 [NASA ADS] [CrossRef] [Google Scholar]
  76. Paszke, A., Gross, S., Massa, F., et al. 2019, arXiv e-prints [arXiv:1912.01703] [Google Scholar]
  77. Piskunov, N. E., Kupka, F., Ryabchikova, T. A., Weiss, W. W., & Jeffery, C. S., 1995, A&AS, 112, 525 [Google Scholar]
  78. Placco, V. M., Frebel, A., Beers, T. C., & Stancliffe, R. J., 2014, ApJ, 797, 21 [Google Scholar]
  79. Plez, B., 2012, Turbospectrum: Code for spectral synthesis, Astrophysics Source Code Library [record ascl:1205.004] [Google Scholar]
  80. Ramírez, I., Meléndez, J., & Chanamé, J., 2012, ApJ, 757, 164 [Google Scholar]
  81. Randich, S., Gilmore, G., Magrini, L., et al. 2022, A&A, 666, A121 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  82. Recio-Blanco, A., de Laverny, P., Palicio, P. A., et al. 2023, A&A, 674, A29 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  83. Ryabchikova, T., Piskunov, N., Kurucz, R. L., et al. 2015, Phys. Scr, 90, 054005 [Google Scholar]
  84. Sánchez-Sáez, P., Lira, H., Martí, L., et al. 2021, AJ, 162, 206 [CrossRef] [Google Scholar]
  85. Santoveña, R., Dafonte, C., & Manteiga, M., 2024, arXiv e-prints [arXiv:2411.05960] [Google Scholar]
  86. Sharma, M., Theuns, T., Frenk, C. S., & Cooke, R. J., 2018, MNRAS, 473, 984 [Google Scholar]
  87. Smee, S. A., Gunn, J. E., Uomoto, A., et al. 2013, AJ, 146, 32 [Google Scholar]
  88. Smith, L. N., & Topin, N., 2019, in Artificial Intelligence and Machine Learning for Multi-domain Operations Applications, 11006, SPIE, 369 [Google Scholar]
  89. Spite, M., Caffau, E., Bonifacio, P., et al. 2013, A&A, 552, A107 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  90. Ting, Y.-S., 2024, arXiv e-prints [arXiv:2412.05806] [Google Scholar]
  91. Ting, Y.-S., Conroy, C., Rix, H.-W., & Cargile, P., 2019, ApJ, 879, 69 [Google Scholar]
  92. Venn, K. A., Irwin, M., Shetrone, M. D., et al. 2004, AJ, 128, 1177 [NASA ADS] [CrossRef] [Google Scholar]
  93. Walsen, K., Jofré, P., Buder, S., et al. 2024, MNRAS, 529, 2946 [NASA ADS] [CrossRef] [Google Scholar]
  94. Wang, X., Chen, H., & Zhu, W., 2023, in Proceedings of the 31st ACM International Conference on Multimedia, MM‘23 (New York, NY, USA: Association for Computing Machinery), 9702 [Google Scholar]
  95. Wheeler, A., Ness, M., Buder, S., et al. 2020, ApJ, 898, 58 [Google Scholar]
  96. Wu, Y., Du, B., Luo, A., Zhao, Y., & Yuan, H., 2014, in IAU Symposium, 306, Statistical Challenges in 21st Century Cosmology, eds. A. Heavens, J.-L. Starck, & A. Krone-Martins, 340 [Google Scholar]
  97. Xiang, M., Ting, Y.-S., Rix, H.-W., et al. 2019, ApJS, 245, 34 [Google Scholar]
  98. Yang, T., & Li, X., 2015, MNRAS, 452, 158 [CrossRef] [Google Scholar]
  99. Yong, D., Norris, J. E., Bessell, M. S., et al. 2013, ApJ, 762, 26 [Google Scholar]
  100. Yoo, H., Lee, Y.-C., Shin, K., & Kim, S.-W., 2023, in Proceedings of the ACM Web Conference 2023, 231 [Google Scholar]
  101. Yoon, J., Beers, T. C., Dietz, S., et al. 2018, ApJ, 861, 146 [NASA ADS] [CrossRef] [Google Scholar]
  102. Zhang, H., Zhang, Y.-F., Liu, W., et al. 2022, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 8024 [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.