EDP Sciences logo
Subscriber Authentication Point
Search
Menu
Home All issues Volume 699 (July 2025) A&A, 699 (2025) A42 References
Open Access
Issue
A&A
Volume 699, July 2025
Article Number A42
Number of page(s) 14
Section Numerical methods and codes
DOI https://doi.org/10.1051/0004-6361/202554518
Published online 01 July 2025
  1. Abdessalem, A. B., Dervilis, N., Wagg, D. J., & Worden, K. 2017, Front. Built Environ., 3 [Google Scholar]
  2. Ahrer, E., Wheatley, P. J., Kirk, J., et al. 2022, MNRAS, 510, 4857 [NASA ADS] [CrossRef] [Google Scholar]
  3. Ahrer, E., Wheatley, P. J., Gandhi, S., et al. 2023, MNRAS, 521, 5636 [Google Scholar]
  4. Aigrain, S., & Foreman-Mackey, D. 2023, Annu. Rev. Astron. Astrophys., 61, 329 [Google Scholar]
  5. Aigrain, S., Pont, F., & Zucker, S. 2012, MNRAS, 419, 3147 [NASA ADS] [CrossRef] [Google Scholar]
  6. Aigrain, S., Hodgkin, S. T., Irwin, M. J., Lewis, J. R., & Roberts, S. J. 2015, MNRAS, 447, 2880 [NASA ADS] [CrossRef] [Google Scholar]
  7. Aigrain, S., Parviainen, H., & Pope, B. J. S. 2016, MNRAS, 459, 2408 [NASA ADS] [Google Scholar]
  8. Akaike, H. 1998, in Selected Papers of Hirotugu Akaike, eds. E. Parzen, K. Tanabe, & G. Kitagawa, Springer Series in Statistics (New York, NY: Springer), 199 [Google Scholar]
  9. Ambikasaran, S., Foreman-Mackey, D., Greengard, L., Hogg, D. W., & O’Neil, M. 2015, IEEE Trans. Pattern Anal. Mach. Intell., 38, 252 [Google Scholar]
  10. Angus, R., Morton, T., Aigrain, S., Foreman-Mackey, D., & Rajpaul, V. 2018, MNRAS, 474, 2094 [Google Scholar]
  11. Bachoc, F. 2013, Computat. Statist. Data Anal., 66, 55 [Google Scholar]
  12. Barros, S. C. C., Demangeon, O., Díaz, R. F., et al. 2020, A&A, 634, A75 [EDP Sciences] [Google Scholar]
  13. Borucki, W. J., Koch, D., Basri, G., et al. 2010, Science, 327, 977 [Google Scholar]
  14. Bradbury, J., Frostig, R., Hawkins, P., et al. 2018, https://doi.org/10.5281/zenodo.10736936 [Google Scholar]
  15. Cabezas, A., Corenflos, A., Lao, J., et al. 2024, arXiv e-prints [arXiv:2402.10797] [Google Scholar]
  16. Caswell, T. A., Elliott Sales de Andrade, Lee, A., et al. 2023, https://doi.org/10.5281/zenodo.592536 [Google Scholar]
  17. Chopin, N., & Papaspiliopoulos, O. 2020, An Introduction to Sequential Monte Carlo, Springer Series in Statistics (Cham: Springer International Publishing) [CrossRef] [Google Scholar]
  18. Crossfield, I. J. M., Ciardi, D. R., Petigura, E. A., et al. 2016, Astrophys. J. Suppl. Ser., 226, 7 [Google Scholar]
  19. Del Moral, P., Doucet, A., & Jasra, A. 2006, J. R. Stat. Soc. Ser. B Stat. Methodol., 68, 411 [Google Scholar]
  20. Duvenaud, D., Lloyd, J. R., Grosse, R., Tenenbaum, J. B., & Ghahramani, Z. 2013, arXiv e-prints [arXiv:1302.4922] [Google Scholar]
  21. Espinoza, N., Kossakowski, D., & Brahm, R. 2019, MNRAS, 490, 2262 [Google Scholar]
  22. Evans, T. M., Pont, F., Sing, D. K., et al. 2013, ApJ, 772, L16 [NASA ADS] [CrossRef] [Google Scholar]
  23. Evans, T. M., Aigrain, S., Gibson, N., et al. 2015, MNRAS, 451, 680 [Google Scholar]
  24. Evans, T. M., Sing, D. K., Kataria, T., et al. 2017, Nature, 548, 58 [NASA ADS] [CrossRef] [Google Scholar]
  25. Foreman-Mackey, D., Montet, B. T., Hogg, D. W., et al. 2015, ApJ, 806, 215 [Google Scholar]
  26. Foreman-Mackey, D., Agol, E., Ambikasaran, S., & Angus, R. 2017a, Astrophys. Source Code Libr. [ascl:1709.008] [Google Scholar]
  27. Foreman-Mackey, D., Agol, E., Ambikasaran, S., & Angus, R. 2017b, AJ, 154, 220 [Google Scholar]
  28. Foreman-Mackey, D., Savel, A., Luger, R., et al. 2021, https://doi.org/10.5281/zenodo.1998447 [Google Scholar]
  29. Foreman-Mackey, D., Yu, W., Yadav, S., et al. 2024, https://doi.org/10.5281/zenodo.10463641 [Google Scholar]
  30. Gardner, J. R., Pleiss, G., Bindel, D., Weinberger, K. Q., & Wilson, A. G. 2018, in Adv. Neural Inf. Process. Syst. [Google Scholar]
  31. Gibson, N. P. 2014, MNRAS, 445, 3401 [NASA ADS] [CrossRef] [Google Scholar]
  32. Gibson, N. P., Aigrain, S., Roberts, S., et al. 2012, MNRAS, 419, 2683 [NASA ADS] [CrossRef] [Google Scholar]
  33. Gillen, E., Briegal, J. T., Hodgkin, S. T., et al. 2020, MNRAS, 492, 1008 [Google Scholar]
  34. Ginsburg, A., Sipőcz, B. M., Brasseur, C. E., et al. 2019, AJ, 157, 98 [Google Scholar]
  35. Hattori, S., Garcia, L., Murray, C., et al. 2024, https://doi.org/10.5281/zenodo.10736936 [Google Scholar]
  36. Haywood, R. D., Collier Cameron, A., Queloz, D., et al. 2014, MNRAS, 443, 2517 [Google Scholar]
  37. Hoffman, M. D., & Gelman, A. 2011, arXiv e-prints [arXiv:1111.4246] [Google Scholar]
  38. Karamanis, M., & Seljak, U. 2025, arXiv e-prints [arXiv:2407.20722] [Google Scholar]
  39. Kim, H., & Teh, Y. W. 2018, arXiv e-prints [arXiv:1706.02524] [Google Scholar]
  40. Leibfried, F., Dutordoir, V., John, S. T., & Durrande, N. 2022, arXiv e-prints [arXiv:2012.13962] [Google Scholar]
  41. Lightkurve Collaboration (Cardoso, J. V. d. M., et al.) 2018, Lightkurve: Kepler and TESS Time Series Analysis in Python, Astrophysics Source Code Library [Google Scholar]
  42. Luger, R., Foreman-Mackey, D., & Hedges, C. 2021, AJ, 162, 124 [NASA ADS] [CrossRef] [Google Scholar]
  43. Nicholson, B. A., & Aigrain, S. 2022, MNRAS, 515, 5251 [NASA ADS] [CrossRef] [Google Scholar]
  44. Ober, S. W., Rasmussen, C. E., & van der Wilk, M. 2021, arXiv e-prints [arXiv:2102.12108] [Google Scholar]
  45. Pinder, T., & Dodd, D. 2022, JOSS, 7, 4455 [Google Scholar]
  46. Pont, F., Zucker, S., & Queloz, D. 2006, MNRAS, 373, 231 [NASA ADS] [CrossRef] [Google Scholar]
  47. Rasmussen, C. E., & Williams, C. K. I. 2006, Gaussian Processes for Machine Learning (The MIT Press) [Google Scholar]
  48. Rauer, H., Catala, C., Aerts, C., et al. 2014, Exp. Astron., 38, 249 [Google Scholar]
  49. Ricker, G. R., Winn, J. N., Vanderspek, R., et al. 2015, J. Astron. Telesc. Instrum. Syst., 1, 014003 [Google Scholar]
  50. Riutort-Mayol, G., Bürkner, P.-C., Andersen, M. R., Solin, A., & Vehtari, A. 2022, Stat. Comput., 33, 17 [Google Scholar]
  51. Saad, F. A., Cusumano-Towner, M. F., Schaechtle, U., Rinard, M. C., & Mansinghka, V. K. 2019, Proc. ACM Program. Lang., 3, 1 [Google Scholar]
  52. Saad, F. A., Patton, B. J., Hoffman, M. D., Saurous, R. A., & Mansinghka, V. K. 2023, arXiv e-prints [arXiv:2307.09607] [Google Scholar]
  53. Samadi, R., Deru, A., Reese, D., et al. 2019, A&A, 624, A117 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  54. Schwarz, G. 1978, Ann. Stat., 6, 461 [Google Scholar]
  55. The pandas development team 2023, https://doi.org/10.5281/zenodo.3509134 [Google Scholar]
  56. Wang, K. A., Pleiss, G., Gardner, J. R., et al. 2019, arXiv e-prints [arXiv:1903.08114] [Google Scholar]
  57. Waskom, M. 2021, JOSS, 6, 3021 [NASA ADS] [CrossRef] [Google Scholar]
  58. Wilson, A. G., Hu, Z., Salakhutdinov, R., & Xing, E. P. 2015, arXiv e-prints [arXiv:1511.02222] [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.