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Home All issues Volume 709 (May 2026) A&A, 709 (2026) A79 References
Open Access
Issue
A&A
Volume 709, May 2026
Article Number A79
Number of page(s) 14
Section Numerical methods and codes
DOI https://doi.org/10.1051/0004-6361/202658903
Published online 05 May 2026
  1. Bacon, R., Brinchmann, J., Conseil, S., et al. 2023, A&A, 670, A4 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  2. Balestra, I., Mainieri, V., Popesso, P., et al. 2010, A&A, 512, A12 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  3. Breiman, L. 2001, Mach. Learn., 45, 5 [Google Scholar]
  4. Cardamone, C. N., van Dokkum, P. G., Urry, C. M., et al. 2010, ApJS, 189, 270 [Google Scholar]
  5. Chambers, K. C., Magnier, E. A., Metcalfe, N., et al. 2016, arXiv e-prints [arXiv:1612.05560] [Google Scholar]
  6. Chen, T., & Guestrin, C. 2016, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16 (New York, NY, USA: ACM), 785 [Google Scholar]
  7. Chen, T., He, T., Benesty, M., et al. 2026, xgboost: Extreme Gradient Boosting, r package version 3.3.0.0 [Google Scholar]
  8. Choi, Y., Olsen, K. A. G., Carlin, J. L., et al. 2025, arXiv e-prints [arXiv:2507.01343] [Google Scholar]
  9. Cooper, M. C., Yan, R., Dickinson, M., et al. 2012, MNRAS, 425, 2116 [NASA ADS] [CrossRef] [Google Scholar]
  10. Damen, M., Labbé, I., van Dokkum, P. G., et al. 2011, ApJ, 727, 1 [NASA ADS] [CrossRef] [Google Scholar]
  11. D’Eugenio, F., Cameron, A. J., Scholtz, J., et al. 2025, ApJS, 277, 4 [Google Scholar]
  12. Duncan, K. J. 2022, MNRAS, 512, 3662 [NASA ADS] [CrossRef] [Google Scholar]
  13. Fadely, R., Hogg, D. W., & Willman, B. 2012, ApJ, 760, 15 [CrossRef] [Google Scholar]
  14. Feng, H.-C., Li, R., Napolitano, N. R., et al. 2025, ApJS, 279, 26 [Google Scholar]
  15. Fotopoulou, S., & Paltani, S. 2018, A&A, 619, A14 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  16. Gaia Collaboration (Prusti, T., et al.) 2016, A&A, 595, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  17. Gaia Collaboration (Vallenari, A., et al.) 2023, A&A, 674, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  18. Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357 [NASA ADS] [CrossRef] [Google Scholar]
  19. Hunter, J. D. 2007, Comput. Sci. Eng., 9, 90 [NASA ADS] [CrossRef] [Google Scholar]
  20. Ivezić, Ž., Kahn, S. M., Tyson, J. A., et al. 2019, ApJ, 873, 111 [Google Scholar]
  21. Jeakel, A. P., Vieira dos Santos, G., Marra, V., et al. 2026, The miniJPAS and J-NEP Surveys: Machine Learning for Star-Galaxy Separation [Google Scholar]
  22. Khramtsov, V., Sergeyev, A., Spiniello, C., et al. 2019, A&A, 632, A56 [EDP Sciences] [Google Scholar]
  23. Kodra, D., Andrews, B. H., Newman, J. A., et al. 2023, ApJ, 942, 36 [NASA ADS] [CrossRef] [Google Scholar]
  24. Kuijken, K., Heymans, C., Dvornik, A., et al. 2019, A&A, 625, A2 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  25. Kurk, J., Cimatti, A., Daddi, E., et al. 2013, A&A, 549, A63 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  26. Le Fèvre, O., Cassata, P., Cucciati, O., et al. 2013, A&A, 559, A14 [Google Scholar]
  27. Lidman, C., Tucker, B. E., Davis, T. M., et al. 2020, MNRAS, 496, 19 [NASA ADS] [CrossRef] [Google Scholar]
  28. Logan, C. H. A., & Fotopoulou, S. 2020, A&A, 633, A154 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  29. Maddox, N., Hewett, P. C., Warren, S. J., & Croom, S. M. 2008, MNRAS, 386, 1605 [Google Scholar]
  30. Momcheva, I. G., Brammer, G. B., van Dokkum, P. G., et al. 2016, ApJS, 225, 27 [Google Scholar]
  31. Mutlu-Pakdil, B., Sand, D. J., Crnojević, D., et al. 2021, ApJ, 918, 88 [CrossRef] [Google Scholar]
  32. NSF-DOE Vera C. Rubin Observatory 2025, Legacy Survey of Space and Time Data Preview 1 [Data set] [Google Scholar]
  33. NSF-DOE Vera C. Rubin Observatory 2025a, Legacy Survey of Space and Time Data Preview 1: Object searchable catalog [Data set] [Google Scholar]
  34. NSF-DOE Vera C. Rubin Observatory 2025b, The Vera C. Rubin Observatory Data Preview 1, Rubin Technical Note RTN-095, NSF-DOE Vera C. Rubin Observatory [Google Scholar]
  35. pandas development team, T. 2020, pandas-dev/pandas: Pandas [Google Scholar]
  36. Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, J. Mach. Learn. Res., 12, 2825 [Google Scholar]
  37. Pennock, C. M., van Loon, J. T., Cioni, M.-R. L., et al. 2025, MNRAS, 537, 1028 [Google Scholar]
  38. Popesso, P., Dickinson, M., Nonino, M., et al. 2009, A&A, 494, 443 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  39. Schlafly, E. F., & Finkbeiner, D. P. 2011, ApJ, 737, 103 [Google Scholar]
  40. Scoville, N., Aussel, H., Brusa, M., et al. 2007, ApJS, 172, 1 [Google Scholar]
  41. Simon, J. D. 2019, ARA&A, 57, 375 [Google Scholar]
  42. Skelton, R. E., Whitaker, K. E., Momcheva, I. G., et al. 2014, ApJS, 214, 24 [Google Scholar]
  43. Smolčić, V., Ivezić, Ž., Knapp, G. R., et al. 2004, ApJ, 615, L141 [Google Scholar]
  44. Talia, M., Schreiber, C., Garilli, B., et al. 2023, A&A, 678, A25 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  45. Taylor, M. 2011, TOPCAT: Tool for OPerations on Catalogues And Tables, Astrophysics Source Code Library [record ascl:1101.010] [Google Scholar]
  46. Tortora, C., Napolitano, N. R., Spavone, M., et al. 2018, MNRAS, 481, 4728 [Google Scholar]
  47. Vanzella, E., Cristiani, S., Dickinson, M., et al. 2008, A&A, 478, 83 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  48. Walsh, S. M., Willman, B., & Jerjen, H. 2009, AJ, 137, 450 [NASA ADS] [CrossRef] [Google Scholar]
  49. Wes McKinney 2010, in Proceedings of the 9th Python in Science Conference, eds. Stéfan van der Walt, & Jarrod Millman, 56 [Google Scholar]
  50. Wolf, C., Meisenheimer, K., Kleinheinrich, M., et al. 2004, A&A, 421, 913 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  51. Wright, A. H., Kuijken, K., Hildebrandt, H., et al. 2024, A&A, 686, A170 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  52. Zhang, S., Hildebrandt, H., Yan, Z., et al. 2025, A&A, 698, A108 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]

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