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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 [CrossRef] [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 [CrossRef] [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]

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