EDP Sciences logo
Subscriber Authentication Point
Search
Menu
Home All issues Volume 673 (May 2023) A&A, 673 (2023) A105 References
Open Access
Issue
A&A
Volume 673, May 2023
Article Number A105
Number of page(s) 15
Section Stellar atmospheres
DOI https://doi.org/10.1051/0004-6361/202243934
Published online 16 May 2023
  1. Abadi, M., Barham, P., Chen, J., et al. 2016, in 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), 265 [Google Scholar]
  2. Allard, F., Homeier, D., & Freytag, B. 2011, ASP Conf. Ser., 448, 91 [Google Scholar]
  3. Andrews, J. J., Chanamé, J., & Agüeros, M. A. 2018, MNRAS, 473, 5393 [NASA ADS] [CrossRef] [Google Scholar]
  4. Antoniadis-Karnavas, A., Sousa, S. G., Delgado-Mena, E., et al. 2020, A&A, 636, A9 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  5. Awang Iskandar, D. N., Zijlstra, A. A., McDonald, I., et al. 2020, Galaxies, 8, 88 [NASA ADS] [CrossRef] [Google Scholar]
  6. Bialek, S., Fabbro, S., Venn, K. A., et al. 2020, MNRAS, 498, 3817 [CrossRef] [Google Scholar]
  7. Bonfils, X., Delfosse, X., Udry, S., et al. 2005, A&A, 442, 635 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  8. Boyajian, T. S., von Braun, K., van Belle, G., et al. 2012, ApJ, 757, 112 [Google Scholar]
  9. Bressan, A., Marigo, P., Girardi, L., et al. 2012, MNRAS, 427, 127 [NASA ADS] [CrossRef] [Google Scholar]
  10. Caballero, J. A., Guàrdia, J., López del Fresno, M., et al. 2016, Proc. SPIE, 9910, 99100E [Google Scholar]
  11. Casagrande, L., Flynn, C., & Bessell, M. 2008, MNRAS, 389, 585 [Google Scholar]
  12. Chen, Y., Girardi, L., Bressan, A., et al. 2014, MNRAS, 444, 2525 [Google Scholar]
  13. Chen, Y., Bressan, A., Girardi, L., et al. 2015, MNRAS, 452, 1068 [Google Scholar]
  14. Cifuentes, C., Caballero, J., Cortés-Contreras, M., et al. 2020, A&A, 642, A115 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  15. Czesla, S., Schröter, S., Schneider, C. P., et al. 2019, Astrophysics Source Code Library, [record ascl:1906.010] [Google Scholar]
  16. Demory, B. O., Ségransan, D., Forveille, T., et al. 2009, A&A, 505, 205 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  17. Desidera, S., Gratton, R. G., Lucatello, S., & Claudi, R. U. 2006, A&A, 454, 581 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  18. Dhital, S., West, A. A., Stassun, K. G., et al. 2012, AJ, 143, 67 [NASA ADS] [CrossRef] [Google Scholar]
  19. Dittmann, J. A., Irwin, J. M., Charbonneau, D., & Newton, E. R. 2016, ApJ, 818, 153 [NASA ADS] [CrossRef] [Google Scholar]
  20. Fabbro, S., Venn, K., O’Briain, T., et al. 2018, MNRAS, 475, 2978 [CrossRef] [Google Scholar]
  21. Gaia Collaboration (Brown, A. G. A., et al.) 2018, A&A, 616, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  22. Gaia Collaboration (Brown, A. G. A., et al.) 2021, A&A, 649, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  23. Gaidos, E., & Mann, A. W. 2014, ApJ, 791, 54 [Google Scholar]
  24. Gaidos, E., Mann, A. W., Lépine, S., et al. 2014, MNRAS, 443, 2561 [Google Scholar]
  25. Gao, Y., & Mosalam, K. M. 2018, Computer-Aided Civil Infrastruc. Eng., 33, 748 [CrossRef] [Google Scholar]
  26. Goodfellow, I., Bengio, Y., & Courville, A. 2016, Deep Learning (Cambridge: MIT Press), [Google Scholar]
  27. Han, X., Zhang, Z., Ding, N., et al. 2021, AI Open, 2, 225 [CrossRef] [Google Scholar]
  28. Hartman, J. D., Bayliss, D., Brahm, R., et al. 2015, AJ, 149, 166 [Google Scholar]
  29. Houdebine, É. R., Mullan, D. J., Doyle, J. G., et al. 2019, AJ, 158, 56 [NASA ADS] [CrossRef] [Google Scholar]
  30. Husser, T.-O., Wende-von Berg, S., Dreizler, S., et al. 2013, A&A, 553, A6 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  31. Johnson, J. A., & Apps, K. 2009, ApJ, 699, 933 [NASA ADS] [CrossRef] [Google Scholar]
  32. Karpathy, A., & Fei-Fei, L. 2015, in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3128 [CrossRef] [Google Scholar]
  33. Khata, D., Mondal, S., Das, R., Ghosh, S., & Ghosh, S. 2020, MNRAS, 493, 4533 [NASA ADS] [CrossRef] [Google Scholar]
  34. Kielty, C. L., Bialek, S., Fabbro, S., et al. 2018, Int. Soc. Opt. Photon., 10707, 107072W [Google Scholar]
  35. Kraus, M., & Feuerriegel, S. 2017, Decision Support Syst., 104, 38 [CrossRef] [Google Scholar]
  36. Lépine, S., Hilton, E. J., Mann, A. W., et al. 2013, AJ, 145, 102 [Google Scholar]
  37. Maldonado, J., Affer, L., Micela, G., et al. 2015, A&A, 577, A132 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  38. Mann, A. W., Brewer, J. M., Gaidos, E., Lépine, S., & Hilton, E. J. 2013a, AJ, 145, 52 [Google Scholar]
  39. Mann, A. W., Gaidos, E., & Ansdell, M. 2013b, ApJ, 779, 188 [NASA ADS] [CrossRef] [Google Scholar]
  40. Mann, A. W., Deacon, N. R., Gaidos, E., et al. 2014, AJ, 147, 160 [CrossRef] [Google Scholar]
  41. Mann, A. W., Feiden, G. A., Gaidos, E., Boyajian, T., & von Braun, K. 2015, ApJ, 804, 64 [Google Scholar]
  42. Marfil, E., Tabernero, H. M., Montes, D., et al. 2021, A&A, 656, A162 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  43. McInnes, L., Healy, J., Saul, N., & Grossberger, L. 2018, J. Open Source Softw., 3, 861 [CrossRef] [Google Scholar]
  44. Minglei, W., Jingchang, P., Zhenping, Y., Xiaoming, K., & Yude, B. 2020, Optik, 218, 165004 [NASA ADS] [CrossRef] [Google Scholar]
  45. Mittal, S., & Vaishay, S. 2019, J. Syst. Architect., 99, 101635 [CrossRef] [Google Scholar]
  46. Montes, D., González-Peinado, R., Tabernero, H. M., et al. 2018, MNRAS, 479, 1332 [Google Scholar]
  47. Nagel, E., Czesla, S., Kaminski, A., et al. 2020, A&A, submitted [Google Scholar]
  48. Neves, V., Bonfils, X., Santos, N. C., et al. 2012, A&A, 538, A25 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  49. Neves, V., Bonfils, X., Santos, N. C., et al. 2014, A&A, 568, A121 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  50. Newton, E. R., Charbonneau, D., Irwin, J., et al. 2014, AJ, 147, 20 [Google Scholar]
  51. Newton, E. R., Charbonneau, D., Irwin, J., & Mann, A. W. 2015, ApJ, 800, 85 [NASA ADS] [CrossRef] [Google Scholar]
  52. O’Briain, T., Ting, Y.-S., Fabbro, S., et al. 2020, ArXiv e-prints [arXiv:2007.03112] [Google Scholar]
  53. Pan, S. J., & Yang, Q. 2010, IEEE Trans. Knowl. Data Eng., 22, 1345 [Google Scholar]
  54. Passegger, V. M., Reiners, A., Jeffers, S. V., et al. 2018, A&A, 615, A6 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  55. Passegger, V. M., Schweitzer, A., Shulyak, D., et al. 2019, A&A, 627, A161 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  56. Passegger, V. M., Bello-García, A., Ordieres-Meré, J., et al. 2020, A&A, 642, A22 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  57. Passegger, V. M., Bello-García, A., Ordieres-Meré, J., et al. 2022, A&A, 658, A194 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  58. Quirrenbach, A., Amado, P. J., Ribas, I., et al. 2018, SPIE Conf. Ser., 10702, 107020W [Google Scholar]
  59. Quirrenbach, A., CARMENES Consortium, Amado, P. J., et al. 2020, SPIE Conf. Ser., 114473, 114473C [Google Scholar]
  60. Rabus, M., Lachaume, R., Jordán, A., et al. 2019, MNRAS, 484, 2674 [Google Scholar]
  61. Raffel, C., Shazeer, N., Roberts, A., et al. 2020, J. Mach. Learn. Res., 21, 1 [Google Scholar]
  62. Refaeilzadeh, P., Tang, L., & Liu, H. 2009, Encyclopedia of Database Systems (Berlin: Springer), 5, 532 [CrossRef] [Google Scholar]
  63. Reiners, A., Zechmeister, M., Caballero, J. A., et al. 2018, A&A, 612, A49 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  64. Rodríguez Martínez, R., Ballard, S., Mayo, A., et al. 2019, AJ, 158, 135 [CrossRef] [Google Scholar]
  65. Rojas-Ayala, B., Covey, K. R., Muirhead, P. S., & Lloyd, J. P. 2010, ApJ, 720, L113 [Google Scholar]
  66. Rojas-Ayala, B., Covey, K. R., Muirhead, P. S., & Lloyd, J. P. 2012, ApJ, 748, 93 [Google Scholar]
  67. Sarkar, D., Bali, R., & Ghosh, T. 2018, Hands-On Transfer Learning with Python: Implement Advanced Deep Learning and Neural Network Models Using TensorFlow and Keras (Birmingham, UK: Packt Publishing Ltd) [Google Scholar]
  68. Schlaufman, K. C., & Laughlin, G. 2010, A&A, 519, A105 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  69. Schweitzer, A., Passegger, V. M., Cifuentes, C., et al. 2019, A&A, 625, A68 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  70. Scott, D. W. 2015, Multivariate Density Estimation: Theory, Practice, and Visualization (Hoboken: John Wiley & Sons) [Google Scholar]
  71. Ségransan, D., Kervella, P., Forveille, T., & Queloz, D. 2003, A&A, 397, L5 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  72. Tabernero, H. M., Marfil, E., Montes, D., & González Hernández, J. I. 2022, A&A, 657, A66 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  73. Tan, C., Sun, F., Kong, T., et al. 2018a, in International Conference on Artificial Neural Networks (Berlin: Springer), 270 [Google Scholar]
  74. Tan, C., Sun, F., Kong, T., et al. 2018b, in Artificial Neural Networks and Machine Learning - ICANN 2018, eds. V. Kurková, Y. Manolopoulos, B. Hammer, L. Iliadis, & I. Maglogiannis (Cham: Springer International Publishing), 270 [CrossRef] [Google Scholar]
  75. Tang, J., Bressan, A., Rosenfield, P., et al. 2014, MNRAS, 445, 4287 [NASA ADS] [CrossRef] [Google Scholar]
  76. Terrell, G. R., & Scott, D.W. 1992, Ann. Stat., 20, 1236 [CrossRef] [Google Scholar]
  77. Terrien, R. C., Mahadevan, S., Bender, C. F., et al. 2012, ApJ, 747, L38 [NASA ADS] [CrossRef] [Google Scholar]
  78. Terrien, R. C., Mahadevan, S., Bender, C. F., Deshpande, R., & Robertson, P. 2015, ApJ, 802, L10 [Google Scholar]
  79. Vabalas, A., Gowen, E., Poliakoff, E., & Casson, A. J. 2019, PloS one, 14, e0224365 [CrossRef] [PubMed] [Google Scholar]
  80. van Leeuwen, F. 2007, A&A, 474, 653 [CrossRef] [EDP Sciences] [Google Scholar]
  81. Vilalta, R. 2018, J. Phys. Conf. Ser., 1085, 052014 [NASA ADS] [CrossRef] [Google Scholar]
  82. von Braun, K., Boyajian, T. S., Kane, S. R., et al. 2011, ApJ, 729, L26 [NASA ADS] [CrossRef] [Google Scholar]
  83. von Braun, K., Boyajian, T. S., Kane, S. R., et al. 2012, ApJ, 753, 171 [NASA ADS] [CrossRef] [Google Scholar]
  84. von Braun, K., Boyajian, T. S., van Belle, G. T., et al. 2014, MNRAS, 438, 2413 [CrossRef] [Google Scholar]
  85. Wang, D., & Li, M. 2017, Information Sci., 412, 210 [CrossRef] [Google Scholar]
  86. Wei, W., Huerta, E., Whitmore, B. C., et al. 2020, MNRAS, 493, 3178 [NASA ADS] [CrossRef] [Google Scholar]
  87. Zechmeister, M., Anglada-Escudé, G., & Reiners, A. 2014, A&A, 561, A59 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  88. Zechmeister, M., Reiners, A., Amado, P. J., et al. 2018, A&A, 609, A12 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  89. Zechmeister, M., Dreizler, S., Ribas, I., et al. 2019, A&A, 627, A49 [NASA ADS] [EDP Sciences] [Google Scholar]
  90. Zhao, Z., Zhang, Q., Yu, X., et al. 2021, IEEE Transactions on Instrumentation and Measurement, 70, 1 [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.