Open Access
| Issue |
A&A
Volume 709, May 2026
|
|
|---|---|---|
| Article Number | A239 | |
| Number of page(s) | 15 | |
| Section | Numerical methods and codes | |
| DOI | https://doi.org/10.1051/0004-6361/202659116 | |
| Published online | 19 May 2026 | |
- Abdalla, F. B., Banerji, M., Lahav, O., & Rashkov, V. 2011, MNRAS, 417, 1891 [NASA ADS] [CrossRef] [Google Scholar]
- Agarwal, S. 2013, in 2013 International Conference on Machine Intelligence and Research Advancement, 203 [Google Scholar]
- Agarwal, N., Dalal, S. R., & Misra, V. 2025, arXiv e-prints [arXiv:2512.22471] [Google Scholar]
- Akiba, T., Sano, S., Yanase, T., Ohta, T., & Koyama, M. 2019, in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining [Google Scholar]
- Ban, Z., Li, X.-B., Yang, X., et al. 2026, Res. Astron. Astrophys., 26, 024002 [Google Scholar]
- Bolzonella, M., Miralles, J. M., & Pelló, R. 2000, A&A, 363, 476 [NASA ADS] [Google Scholar]
- Brammer, G. B., van Dokkum, P. G., & Coppi, P. 2008, ApJ, 686, 1503 [Google Scholar]
- Breiman, L. 2001, J. Clinical Microbiol., 2, 199 [Google Scholar]
- Cao, W., Wang, D., Li, J., et al. 2018a, in Advances in Neural Information Processing Systems (New York: Curran Associates, Inc.), 31 [Google Scholar]
- Cao, Y., Gong, Y., Meng, X.-M., et al. 2018b, MNRAS, 480, 2178 [Google Scholar]
- Cao, Y., Gong, Y., Zheng, Z.-Y., & Xu, C. 2022, Res. Astron. Astrophys., 22, 025019 [Google Scholar]
- Carrasco Kind, M., & Brunner, R. J. 2013, MNRAS, 432, 1483 [Google Scholar]
- Chartab, N., Mobasher, B., Cooray, A. R., et al. 2023, ApJ, 942, 91 [Google Scholar]
- Cole, S., Percival, W. J., Peacock, J. A., et al. 2005, MNRAS, 362, 505 [Google Scholar]
- Conroy, C., Gunn, J. E., & White, M. 2009, ApJ, 699, 486 [Google Scholar]
- Conselice, C. J. 2014, ARA&A, 52, 291 [CrossRef] [Google Scholar]
- Cover, T., & Hart, P. 1967, IEEE Trans. Inform. Theory, 13, 21 [CrossRef] [Google Scholar]
- CSST Collaboration (Gong, Y., et al.) 2026, Sci. China Phys. Mech. Astron., 69, 239501 [Google Scholar]
- Demirtas, H. 2018, J. Stat. Softw. Book Rev., 85, 1 [Google Scholar]
- Desprez, G., Paltani, S., Coupon, J., et al. 2020, A&A, 644, A31 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Dey, A., Schlegel, D. J., Lang, D., et al. 2019, AJ, 157, 168 [Google Scholar]
- Du, W., Côté, D., & Liu, Y. 2023a, Expert Syst. Appl., 219, 119619 [Google Scholar]
- Du, W., Yang, Y., Qian, L., Wang, J., & Wen, Q. 2023b, arXiv e-prints [arXiv:2305.18811] [Google Scholar]
- Euclid Collaboration (Tucci, M., et al.) 2025, A&A, accepted [arXiv:2503.15306] [Google Scholar]
- Feldmann, R., Carollo, C. M., Porciani, C., et al. 2006, MNRAS, 372, 565 [NASA ADS] [CrossRef] [Google Scholar]
- Fortuin, V., Baranchuk, D., Raetsch, G., & Mandt, S. 2020, in Proceedings of Machine Learning Research, 108, Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, ed. S. Chiappa & R. Calandra (PMLR), 1651 [Google Scholar]
- Fotopoulou, S., & Paltani, S. 2018, A&A, 619, A14 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Gong, Y., Liu, X., Cao, Y., et al. 2019, ApJ, 883, 203 [NASA ADS] [CrossRef] [Google Scholar]
- Graham, J. W. 2009, Ann. Rev. Psychol., 60, 549 [Google Scholar]
- Han, J., Li, M., Jiang, W., et al. 2025, Sci. China Phys. Mech. Astron., 68, 109511 [Google Scholar]
- Ilbert, O., Arnouts, S., McCracken, H. J., et al. 2006, A&A, 457, 841 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Ivezić, Z., Kahn, S. M., Tyson, J. A., et al. 2019, ApJ, 873, 111 [NASA ADS] [CrossRef] [Google Scholar]
- Keerin, P., & Boongoen, T. 2022, Inform. Process. Management, 59, 102881 [Google Scholar]
- Koo, D. C. 1985, AJ, 90, 418 [NASA ADS] [CrossRef] [Google Scholar]
- La Torre, V., Sajina, A., Goulding, A. D., et al. 2024, AJ, 167, 261 [Google Scholar]
- Laureijs, R., Amiaux, J., Arduini, S., et al. 2011, arXiv e-prints [arXiv:1110.3193] [Google Scholar]
- Little, R., & Rubin, D. 2019, Statistical Analysis with Missing Data, 3rd edn. (Hoboken: Wiley) [Google Scholar]
- Liu, D. Z., Meng, X. M., Er, X. Z., et al. 2023, A&A, 669, A128 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Liu, Y., Hu, T., Zhang, H., et al. 2024, in The Twelfth International Conference on Learning Representations [Google Scholar]
- Loh, E. D., & Spillar, E. J. 1986, ApJ, 303, 154 [NASA ADS] [CrossRef] [Google Scholar]
- LSST Science Collaboration (Abell, P. A., et al.) 2009, arXiv e-prints [arXiv:0912.0201] [Google Scholar]
- Luken, K. J., Padhy, R., & Wang, X. R. 2021, in Machine Learning for Physical Sciences workshop at NeurIPS 2021, 1 [Google Scholar]
- Luo, Z., Tang, Z., Chen, Z., et al. 2024, MNRAS, 531, 3539 [Google Scholar]
- Ma, Z., Tian, H., Liu, Z., & Zhang, Z. 2020, Appl. Soft Comput., 90, 106175 [Google Scholar]
- Miao, X., Wu, Y., Wang, J., et al. 2021, Proc. AAAI Conf. Artif. Intell., 35, 8983 [Google Scholar]
- Mo, H., van den Bosch, F. C., & White, S. 2010, Galaxy Formation and Evolution (Cambridge, UK: Cambridge University Press) [Google Scholar]
- Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, J. Mach. Learn. Res., 12, 2825 [Google Scholar]
- Percival, W. J., Nichol, R. C., Eisenstein, D. J., et al. 2007, ApJ, 657, 645 [NASA ADS] [CrossRef] [Google Scholar]
- Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. 2018, in Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS’18 (Red Hook, NY, USA: Curran Associates Inc.), 6639 [Google Scholar]
- Salvato, M., Ilbert, O., & Hoyle, B. 2019, Nat. Astron., 3, 212 [NASA ADS] [CrossRef] [Google Scholar]
- Schindler, J.-T., Fan, X., McGreer, I. D., et al. 2017, ApJ, 851, 13 [NASA ADS] [CrossRef] [Google Scholar]
- Tasca, L. A. M., Kneib, J. P., Iovino, A., et al. 2009, A&A, 503, 379 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Van Buuren, S. 2000, Multivariate imputation by chained equations: MICE V1. 0 user’s manual (Leiden: TNO) [Google Scholar]
- Vaswani, A., Shazeer, N., Parmar, N., et al. 2017, in Advances in Neural Information Processing Systems, eds. I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (New York: Curran Associates, Inc.), 30 [Google Scholar]
- Venkatraman, R., & Khaitan, S. K. 2015, in 2015 IEEE Power & Energy Society General Meeting, 996 [Google Scholar]
- Veronika Dorogush, A., Ershov, V., & Gulin, A. 2018, arXiv e-prints [arXiv:1810.11363] [Google Scholar]
- Wang, A., Chen, Y., An, N., et al. 2019, IEEE/ACM Trans. Comput. Biol. Bioinform., 16, 980 [Google Scholar]
- Wei, C.-L., Li, G.-L., Fang, Y.-D., et al. 2026a, Res. Astron. Astrophys., 26, 024001 [Google Scholar]
- Wei, C.-L., Luo, Y., Tian, H., et al. 2026b, Res. Astron. Astrophys., 26, 024004 [Google Scholar]
- Xian, J.-T., Lin, L., Fang, Y.-D., et al. 2026, Res. Astron. Astrophys., 26, 024005 [Google Scholar]
- Yoon, J., Jordon, J., & Schaar, M. 2018, in International conference on machine learning, PMLR, 5689 [Google Scholar]
- Yoon, J., Zame, W. R., & van der Schaar, M. 2019, IEEE Trans. Biomed. Eng., 66, 1477 [Google Scholar]
- Zhan, H. 2011, Scientia Sinica Physica, Mechanica & Astronomica, 41, 1441 [Google Scholar]
- Zhan, H. 2021, Chinese Sci. Bull., 66, 1290 [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.