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Home All issues Volume 691 (November 2024) A&A, 691 (2024) A218 References
Open Access
Issue
A&A
Volume 691, November 2024
Article Number A218
Number of page(s) 17
Section Stellar atmospheres
DOI https://doi.org/10.1051/0004-6361/202450164
Published online 15 November 2024
  1. Abadi, M., Agarwal, A., Barham, P., et al. 2015, TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, software available from tensorflow.org [Google Scholar]
  2. Abdurro’uf, Accetta, K., Aerts, C., et al. 2022, ApJS, 259, 35 [NASA ADS] [CrossRef] [Google Scholar]
  3. Andrae, R., Fouesneau, M., Sordo, R., et al. 2023, A&A, 674, A27 [CrossRef] [EDP Sciences] [Google Scholar]
  4. Astropy Collaboration (Robitaille, T. P., et al.) 2013, A&A, 558, A33 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  5. Astropy Collaboration (Price-Whelan, A. M., et al.) 2018, AJ, 156, 123 [Google Scholar]
  6. Astropy Collaboration (Price-Whelan, A. M., et al.) 2022, ApJ, 935, 167 [NASA ADS] [CrossRef] [Google Scholar]
  7. Baglin, A., Auvergne, M., Boisnard, L., et al. 2006, in 36th COSPAR Scientific Assembly, 36, 3749 [Google Scholar]
  8. Bailer-Jones, C. A. L., Rybizki, J., Fouesneau, M., Demleitner, M., & Andrae, R. 2021, AJ, 161, 147 [Google Scholar]
  9. Bordé, P., Rouan, D., & Léger, A. 2003, A&A, 405, 1137 [Google Scholar]
  10. Borucki, W. J., Koch, D., Basri, G., et al. 2010, Science, 327, 977 [Google Scholar]
  11. Bressan, A., Marigo, P., Girardi, L., et al. 2012, MNRAS, 427, 127 [NASA ADS] [CrossRef] [Google Scholar]
  12. Buder, S., Sharma, S., Kos, J., et al. 2021, MNRAS, 506, 150 [NASA ADS] [CrossRef] [Google Scholar]
  13. Burnett, B., & Binney, J. 2010, MNRAS, 407, 339 [NASA ADS] [CrossRef] [Google Scholar]
  14. Carlin, J. L., Liu, C., Newberg, H. J., et al. 2015, AJ, 150, 4 [NASA ADS] [CrossRef] [Google Scholar]
  15. Chollet, F. et al. 2015, Keras, https://github.com/fchollet/keras [Google Scholar]
  16. Cui, X.-Q., Zhao, Y.-H., Chu, Y.-Q., et al. 2012, RAA, 12, 1197 [NASA ADS] [Google Scholar]
  17. Das, P., & Sanders, J. L. 2019, MNRAS, 484, 294 [NASA ADS] [CrossRef] [Google Scholar]
  18. Dorogush, A. V., Ershov, V., & Gulin, A. 2018, arXiv e-prints [arXiv: 1810.11363] [Google Scholar]
  19. El-Badry, K., Rix, H.-W., Ting, Y.-S., et al. 2018, MNRAS, 473, 5043 [Google Scholar]
  20. Erickson, N., Mueller, J., Shirkov, A., et al. 2020, arXiv e-prints [arXiv:2003.06505] [Google Scholar]
  21. Fabbro, S., Venn, K. A., O’Briain, T., et al. 2018, MNRAS, 475, 2978 [Google Scholar]
  22. Frankel, N., Sanders, J., Rix, H.-W., Ting, Y.-S., & Ness, M. 2019, ApJ, 884, 99 [Google Scholar]
  23. Gaia Collaboration (Recio-Blanco, A., et al.) 2023a, A&A, 674, A38 [CrossRef] [EDP Sciences] [Google Scholar]
  24. Gaia Collaboration (Vallenari, A., et al.) 2023b, A&A, 674, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  25. Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357 [NASA ADS] [CrossRef] [Google Scholar]
  26. Hawkins, K. 2023, MNRAS, 525, 3318 [NASA ADS] [CrossRef] [Google Scholar]
  27. Hawley, S. L., Covey, K. R., Knapp, G. R., et al. 2002, AJ, 123, 3409 [NASA ADS] [CrossRef] [Google Scholar]
  28. Hunter, J. D. 2007, Comput. Sci. Eng., 9, 90 [NASA ADS] [CrossRef] [Google Scholar]
  29. Imig, J., Price, C., Holtzman, J. A., et al. 2023, ApJ, 954, 124 [CrossRef] [Google Scholar]
  30. Ioffe, S., & Szegedy, C. 2015, arXiv e-prints [arXiv: 1502.03167] [Google Scholar]
  31. Ke, G., Meng, Q., Finley, T., et al. 2017, Adv. Neural Inform. Process. Syst., 30 [Google Scholar]
  32. Kingma, D. P., & Ba, J. 2014, arXiv e-prints [arXiv: 1412.6980] [Google Scholar]
  33. Leung, H. W., & Bovy, J. 2019a, MNRAS, 483, 3255 [NASA ADS] [Google Scholar]
  34. Leung, H. W., & Bovy, J. 2019b, MNRAS, 489, 2079 [CrossRef] [Google Scholar]
  35. Lindegren, L., Bastian, U., Biermann, M., et al. 2021, A&A, 649, A4 [EDP Sciences] [Google Scholar]
  36. Lundberg, S., & Lee, S.-I. 2017, arXiv e-prints [arXiv:1705.07874] [Google Scholar]
  37. Luo, A.-L., Zhao, Y.-H., Zhao, G., et al. 2015, RAA, 15, 1095 [NASA ADS] [Google Scholar]
  38. Majewski, S. R., Schiavon, R. P., Frinchaboy, P. M., et al. 2017, AJ, 154, 94 [NASA ADS] [CrossRef] [Google Scholar]
  39. Önal Tas, Ö., Bilir, S., Seabroke, G., et al. 2016, PASP, 33, e044 [Google Scholar]
  40. O’Briain, T., Ting, Y.-S., Fabbro, S., et al. 2021, ApJ, 906, 130 [CrossRef] [Google Scholar]
  41. Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, J. Mach. Learn. Res., 12, 2825 [Google Scholar]
  42. Queiroz, A. B. A., Anders, F., Santiago, B. X., et al. 2018, MNRAS, 476, 2556 [Google Scholar]
  43. Queiroz, A. B. A., Anders, F., Chiappini, C., et al. 2020, A&A, 638, A76 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  44. Ricker, G. R., Winn, J. N., Vanderspek, R., et al. 2015, J. Astron. Telesc. Instrum. Syst., 1, 014003 [Google Scholar]
  45. Rui, W., Ali, L., Shuo, Z., et al. 2019, PASP, 131, 024505 [NASA ADS] [CrossRef] [Google Scholar]
  46. Santiago, B. X., Brauer, D. E., Anders, F., et al. 2016, A&A, 585, A42 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  47. Stone-Martinez, A., Holtzman, J. A., Imig, J., et al. 2024, AJ, 167, 73 [NASA ADS] [CrossRef] [Google Scholar]
  48. Taylor, M. B. 2005, in Astronomical Society of the Pacific Conference Series, 347, Astronomical Data Analysis Software and Systems XIV, eds. P. Shopbell, M. Britton, & R. Ebert, 29 [NASA ADS] [Google Scholar]
  49. Ting, Y.-S., Conroy, C., Rix, H.-W., & Cargile, P. 2019, ApJ, 879, 69 [Google Scholar]
  50. Ulla, A., Creevey, O. L., Álvarez, M. A., et al. 2022, Gaia DR3 documentation Chapter 11: Astrophysical parameters [Google Scholar]
  51. Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261 [CrossRef] [Google Scholar]
  52. Wang, R., Luo, A.-L., Chen, J.-J., et al. 2020, ApJ, 891, 23 [NASA ADS] [CrossRef] [Google Scholar]
  53. Wang, C., Yuan, H., Xiang, M., et al. 2023a, A&A, 674, A129 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  54. Wang, R., Luo, A.-L., Zhang, S., et al. 2023b, ApJS, 266, 40 [NASA ADS] [CrossRef] [Google Scholar]
  55. Wes McKinney 2010, in Proceedings of the 9th Python in Science Conference, eds. Stéfan van der Walt, & Jarrod Millman, 56 [Google Scholar]
  56. Xiang, M., Rix, H.-W., Ting, Y.-S., et al. 2022, A&A, 662, A66 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  57. Yan, Y., Du, C., Liu, S., et al. 2019, ApJ, 880, 36 [NASA ADS] [CrossRef] [Google Scholar]
  58. Zhang, X., Zhao, G., Yang, C. Q., Wang, Q. X., & Zuo, W. B. 2019, PASP, 131, 094202 [CrossRef] [Google Scholar]
  59. Zhang, B., Liu, C., & Deng, L.-C. 2020, ApJS, 246, 9 [NASA ADS] [CrossRef] [Google Scholar]
  60. Zhang, W., Zhang, J., He, H., et al. 2022, ApJS, 263, 12 [NASA ADS] [CrossRef] [Google Scholar]
  61. Zhao, G., Zhao, Y.-H., Chu, Y.-Q., Jing, Y.-P., & Deng, L.-C. 2012, RAA, 12, 723 [Google Scholar]

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