Open Access
Issue
A&A
Volume 700, August 2025
Article Number A190
Number of page(s) 23
Section Planets, planetary systems, and small bodies
DOI https://doi.org/10.1051/0004-6361/202554401
Published online 19 August 2025
  1. Abadi, M., Agarwal, A., Barham, P., et al. 2015, arXiv e-prints [arXiv:1603.04467] [Google Scholar]
  2. Andrews, S. M., Wilner, D. J., Zhu, Z., et al. 2016, AJ, 820, L40 [Google Scholar]
  3. Andrews, S. M., Huang, J., Pérez, L. M., et al. 2018, ApJ, 869, L41 [NASA ADS] [CrossRef] [Google Scholar]
  4. Asensio-Torres, R., Henning, T., Cantalloube, F., et al. 2021, A&A, 652, A101 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  5. Auddy, S., Dey, R., Lin, M.-K., Carrera, D., & Simon, J. B., 2022, ApJ, 936, 93 [NASA ADS] [CrossRef] [Google Scholar]
  6. Auddy, S., & Lin, M.-K., 2020, AJ, 900, 62 [Google Scholar]
  7. Auddy, S., Dey, R., Lin, M.-K., & Hall, C., 2021, ApJ, 920, 3 [NASA ADS] [CrossRef] [Google Scholar]
  8. Bae, J., Isella, A., Zhu, Z., et al. 2023, ASP Conf. Ser., 534, 423 [NASA ADS] [Google Scholar]
  9. Barge, P., Ricci, L., Carilli, C. L., & Previn-Ratnasingam, R., 2017, A&A, 605, A122 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  10. Baruteau, C., Barraza, M., Pérez, S., et al. 2019, MNRAS, 486, 304 [Google Scholar]
  11. Benisty, M., Bae, J., Facchini, S., et al. 2021, ApJ, 916, L2 [NASA ADS] [CrossRef] [Google Scholar]
  12. Benitez-Llambay, P., & Masset, F. S., 2016, AJ Suppl. Ser., 223, 11 [Google Scholar]
  13. Benítez-Llambay, P., Krapp, L., & Pessah, M. E., 2019, AJ Suppl. Ser., 241, 25 [Google Scholar]
  14. Chollet, F., et al. 2015, Keras, https://keras.io [Google Scholar]
  15. Christiaens, V., Cantalloube, F., Casassus, S., et al. 2019, ApJ, 877, L33 [Google Scholar]
  16. Clarke, C. J., Tazzari, M., Juhasz, A., et al. 2018, AJ, 866, L6 [Google Scholar]
  17. Currie, T., Lawson, K., Schneider, G., et al. 2022, Nat. Astron., 6, 751 [NASA ADS] [CrossRef] [Google Scholar]
  18. Dipierro, G., & Laibe, G., 2017, MNRAS, 469, 1932 [Google Scholar]
  19. Dipierro, G., Price, D., Laibe, G., et al. 2015, MNRAS, 453, L73 [NASA ADS] [CrossRef] [Google Scholar]
  20. Dong, R., & Fung, J., 2017, AJ, 835, 146 [Google Scholar]
  21. Dong, R., Li, S., Chiang, E., & Li, H., 2018, AJ, 866, 110 [Google Scholar]
  22. Duffell, P. C., & Macfadyen, A. I., 2013, AJ, 769, 41 [Google Scholar]
  23. Dullemond, C. P., & Penzlin, A. B. T., 2018, A&A, 609, A50 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  24. Facchini, S., Benisty, M., Bae, J., et al. 2020, A&A, 639, A121 [EDP Sciences] [Google Scholar]
  25. Fedele, D., Tazzari, M., Booth, R., et al. 2018, A&A, 610, A24 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  26. Fedele, D., Bollati, F., & Lodato, G., 2023, A&A, 672, A125 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  27. Flaherty, K. M., Hughes, A. M., Rosenfeld, K. A., et al. 2015, AJ, 813, 99 [Google Scholar]
  28. Flaherty, K., Hughes, A. M., Simon, J. B., et al. 2020, AJ, 895, 109 [Google Scholar]
  29. Flaherty, K., Hughes, A. M., Simon, J. B., et al. 2024, MNRAS, 532, 363 [NASA ADS] [CrossRef] [Google Scholar]
  30. Gabry, J., Simpson, D., Vehtari, A., Betancourt, M., & Gelman, A., 2019, J. R. Stat. Soc. Ser., 182, 389 [Google Scholar]
  31. Gal, Y., & Ghahramani, Z., 2016, Proceedings of The 33rd International Conference on Machine Learning, eds. M. F. Balcan & K. Q. Weinberger (New York, USA: PMLR), 48, 1050 [Google Scholar]
  32. Gratton, R., Ligi, R., Sissa, E., et al. 2019, A&A, 623, A140 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  33. Haffert, S. Y., Bohn, A. J., de Boer, J., et al. 2019, Nat. Astron., 3, 749 [Google Scholar]
  34. Hammond, I., Christiaens, V., Price, D. J., et al. 2023, MNRAS, 522, L51 [NASA ADS] [CrossRef] [Google Scholar]
  35. Hashimoto, J., Muto, T., Dong, R., et al. 2021, AJ, 911, 5 [Google Scholar]
  36. Hawley, J. F., 2001, AJ, 554, 534 [Google Scholar]
  37. He, K., Zhang, X., Ren, S., & Sun, J., 2016, in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1 [Google Scholar]
  38. Hu, X., Zhu, Z., Okuzumi, S., et al. 2019, ApJ, 885, 36 [Google Scholar]
  39. Huang, J., Andrews, S. M., Dullemond, C. P., et al. 2020a, AJ, 891, 48 [Google Scholar]
  40. Huang, J., Andrews, S. M., Öberg, K. I., et al. 2020b, AJ, 898, 140 [Google Scholar]
  41. Isella, A., Guidi, G., Testi, L., et al. 2016, PRL, 117, 251101 [Google Scholar]
  42. Isella, A., Benisty, M., Teague, R., et al. 2019, AJ, 879, L25 [Google Scholar]
  43. Kanagawa, K. D., Tanaka, H., Muto, T., Tanigawa, T., & Takeuchi, T., 2015, MNRAS, 448, 994 [NASA ADS] [CrossRef] [Google Scholar]
  44. Kanagawa, K. D., Muto, T., Tanaka, H., et al. 2016, PASJ, 68, 43 [NASA ADS] [Google Scholar]
  45. Kendall, A., & Gal, Y., 2017, Advances in Neural Information Processing Systems, 5575 [Google Scholar]
  46. Keppler, M., Teague, R., Bae, J., et al. 2019, A&A, 625, A118 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  47. Kim, S., Takahashi, S., Nomura, H., et al. 2020, AJ, 888, 72 [Google Scholar]
  48. Le Folgoc, L., Baltatzis, V., Desai, S., et al. 2021, arXiv e-prints [arXiv:2110.04286] [Google Scholar]
  49. Lemos, P., Coogan, A., Hezaveh, Y., & Perreault-Levasseur, L. 2023a, 40th International Conference on Machine Learning, 202, 19256 [Google Scholar]
  50. Lemos, P., Parker, L. H., Hahn, C., et al. 2023b, in Machine Learning for Astrophysics, 18 [Google Scholar]
  51. Lodato, G., Dipierro, G., Ragusa, E., et al. 2019, MNRAS, 486, 453 [Google Scholar]
  52. Lodato, G., Rampinelli, L., Viscardi, E., et al. 2022, MNRAS, 518, 4481 [NASA ADS] [CrossRef] [Google Scholar]
  53. Long, F., Pinilla, P., Herczeg, G. J., et al. 2018, AJ, 869, 17 [Google Scholar]
  54. Longarini, C., Lodato, G., Rosotti, G., et al. 2025, ApJ, 984, L17 [Google Scholar]
  55. Manara, C. F., Testi, L., Natta, A., et al. 2014, A&A, 568, A18 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  56. Manara, C. F., Ansdell, M., Rosotti, G. P., et al. 2023, ASPC, 534, 539 [Google Scholar]
  57. Mao, S., Dong, R., Lu, L., et al. 2023, ApJ, 950, L12 [CrossRef] [Google Scholar]
  58. Mao, S., Dong, R., Yi, K. M., et al. 2024, ApJ, 976, 200 [Google Scholar]
  59. Marino, S., 2021, MNRAS, 503, 5100 [NASA ADS] [CrossRef] [Google Scholar]
  60. Martire, P., Longarini, C., Lodato, G., et al. 2024, A&A, 686, A9 [Google Scholar]
  61. Mesa, D., Keppler, M., Cantalloube, F., et al. 2019, A&A, 632, A25 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  62. Nazari, P., Booth, R. A., Clarke, C. J., et al. 2019, MNRAS, 485, 5914 [Google Scholar]
  63. Nielsen, E. L., De Rosa, R. J., Macintosh, B., et al. 2019, AJ, 158, 13 [Google Scholar]
  64. Öberg, K. I., Guzmán, V. V., Walsh, C., et al. 2021, ApJS, 257, 1 [CrossRef] [Google Scholar]
  65. Pascucci, I., Cabrit, S., Edwards, S., et al. 2023, ASPC, 534, 567 [Google Scholar]
  66. Pérez, S., Casassus, S., Baruteau, C., et al. 2019, AJ, 158, 15 [Google Scholar]
  67. Pinte, C., 2019, HD 97048 ALMA B7 continuum + 13CO, https://doi.org/10.6084/m9.figshare.8266988.v1 [Google Scholar]
  68. Pinte, C., van der Plas, G., Ménard, F., et al. 2019, Nat. Astron., 3, 1109 [NASA ADS] [CrossRef] [Google Scholar]
  69. Reggiani, M., Meyer, M. R., Chauvin, G., et al. 2016, A&A, 586, A147 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  70. Rosotti, G. P., 2023, New Astron. Rev., 96, 101674 [NASA ADS] [CrossRef] [Google Scholar]
  71. Rosotti, G. P., Juhasz, A., Booth, R. A., & Clarke, C. J., 2016, MNRAS, 459, 2790 [Google Scholar]
  72. Ruzza, A., Lodato, G., & Rosotti, G. P., 2024, A&A, 685, A65 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  73. Scardoni, C. E., Clarke, C. J., Rosotti, G. P., et al. 2022, MNRAS, 514, 5478 [NASA ADS] [CrossRef] [Google Scholar]
  74. Teague, R., Guilloteau, S., Semenov, D., et al. 2016, A&A, 592, A49 [CrossRef] [EDP Sciences] [Google Scholar]
  75. Tejero-Cantero, A., Boelts, J., Deistler, M., et al. 2020, J. Open Source Softw., 5, 2505 [NASA ADS] [CrossRef] [Google Scholar]
  76. Toci, C., Lodato, G., Fedele, D., Testi, L., & Pinte, C., 2019, AJ, 888, L4 [Google Scholar]
  77. Toci, C., Lodato, G., Christiaens, V., et al. 2020, MNRAS, 499, 2015 [NASA ADS] [CrossRef] [Google Scholar]
  78. van Terwisga, S. E., van Dishoeck, E. F., Ansdell, M., et al. 2018, A&A, 616, A88 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  79. Veronesi, B., Ragusa, E., Lodato, G., et al. 2020, Eur. Planet. Sci. Congress, 2020–398 [Google Scholar]
  80. Vigan, A., Fontanive, C., Meyer, M., et al. 2021, A&A, 651, A72 [EDP Sciences] [Google Scholar]
  81. Wagner, K., Follette, K. B., Close, L. M., et al. 2018, ApJ, 863, L8 [NASA ADS] [CrossRef] [Google Scholar]
  82. Wallack, N. L., Ruffio, J.-B., Ruane, G., et al. 2024, AJ, 168, 78 [Google Scholar]
  83. Wang, S., Kanagawa, K. D., & Suto, Y., 2021, AJ, 923, 165 [Google Scholar]
  84. Zhang, S., Zhu, Z., Huang, J., et al. 2018, AJ, 869, L47 [Google Scholar]
  85. Zhang, S., Zhu, Z., & Kang, M., 2022, MNRAS, 510, 4473 [NASA ADS] [CrossRef] [Google Scholar]
  86. Zhou, Y., Bowler, B. P., Wagner, K. R., et al. 2021, AJ, 161, 244 [NASA ADS] [CrossRef] [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.