Open Access
Issue |
A&A
Volume 681, January 2024
|
|
---|---|---|
Article Number | A123 | |
Number of page(s) | 11 | |
Section | Cosmology (including clusters of galaxies) | |
DOI | https://doi.org/10.1051/0004-6361/202346734 | |
Published online | 23 January 2024 |
- Alipour, N., Safari, H., & Innes, D. E. 2012, ApJ, 746, 12 [NASA ADS] [CrossRef] [Google Scholar]
- Alves de Oliveira, R., Li, Y., Villaescusa-Navarro, F., Ho, S., & Spergel, D. N. 2020, ArXiv e-prints [arXiv:2012.00240] [Google Scholar]
- Angel, J. R. P., Wizinowich, P., Lloyd-Hart, M., & Sandler, D. 1990, Nature, 348, 221 [NASA ADS] [CrossRef] [Google Scholar]
- Bailer-Jones, C. A. L., Smith, K. W., Tiede, C., Sordo, R., & Vallenari, A. 2008, MNRAS, 391, 1838 [NASA ADS] [CrossRef] [Google Scholar]
- Bailer-Jones, C. A. L., Andrae, R., Arcay, B., et al. 2013, A&A, 559, A74 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Bailey, S., Aragon, C., Romano, R., et al. 2007, ApJ, 665, 1246 [NASA ADS] [CrossRef] [Google Scholar]
- Bardeen, J. M., Bond, J. R., Kaiser, N., & Szalay, A. S. 1986, ApJ, 304, 15 [Google Scholar]
- Beaumont, C. N., Williams, J. P., & Goodman, A. A. 2011, ApJ, 741, 14 [NASA ADS] [CrossRef] [Google Scholar]
- Bellm, E., & Kulkarni, S. 2017, Nat. Astron., 1, 0071 [NASA ADS] [CrossRef] [Google Scholar]
- Bellman, R., Bellman, R., & Corporation, R. 1957, Dynamic Programming, Rand Corporation Research Study (Princeton: Princeton University Press) [Google Scholar]
- Bergstra, J., & Bengio, Y. 2012, J. Mach. Learn. Res., 13, 281 [Google Scholar]
- Breiman, L. 1996, Mach. Learn., 24, 123 [Google Scholar]
- Breiman, L. 2001, Mach. Learn., 45, 5 [Google Scholar]
- Breiman, L., Last, M., & Rice, J. 2003, in Statistical Challenges in Astronomy, eds. E. D. Feigelson, & G. J. Babu, 243 [CrossRef] [Google Scholar]
- Carliles, S., Budavári, T., Heinis, S., Priebe, C., & Szalay, A. S. 2010, ApJ, 712, 511 [NASA ADS] [CrossRef] [Google Scholar]
- Colombi, S., Jaffe, A., Novikov, D., & Pichon, C. 2009, MNRAS, 393, 511 [NASA ADS] [CrossRef] [Google Scholar]
- Conceição, M., Krone-Martins, A., & da Silva, A. 2021, in 2021 IEEE 17th International Conference on eScience (eScience), 225 [CrossRef] [Google Scholar]
- Conceição, M., Krone-Martins, A., & Da Silva, A. 2022, in 2022 IEEE 18th International Conference on e-Science (e-Science), 395 [CrossRef] [Google Scholar]
- Cortes, C., & Vapnik, V. 1995, Mach. Learn., 20, 273 [Google Scholar]
- Couchman, H. M. P., Thomas, P. A., & Pearce, F. R. 1995, ApJ, 452, 797 [NASA ADS] [CrossRef] [Google Scholar]
- Currin, C., Mitchell, T., Morris, M., & Ylvisaker, D. 1988, ORNL Tech. Rep., ORNL-6498, TRN: US200318%%70 [Google Scholar]
- Currin, C., Mitchell, T., Morris, M., & Ylvisaker, D. 1991, J. Am. Stat. Assoc., 86, 953 [CrossRef] [Google Scholar]
- da Silva, A. C., Barbosa, D., Liddle, A. R., & Thomas, P. A. 2001, MNRAS, 326, 155 [NASA ADS] [CrossRef] [Google Scholar]
- Delchambre, L. 2018, MNRAS, 473, 1785 [NASA ADS] [CrossRef] [Google Scholar]
- Delchambre, L., Krone-Martins, A., Wertz, O., et al. 2019, A&A, 622, A165 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Djorgovski, S. G., Mahabal, A. A., Graham, M. J., Polsterer, K., & Krone-Martins, A. 2022, ArXiv e-prints [arXiv:2212.01493] [Google Scholar]
- Dubath, P., Rimoldini, L., Süveges, M., et al. 2011, MNRAS, 414, 2602 [Google Scholar]
- Ducourant, C., Teixeira, R., Krone-Martins, A., et al. 2017, A&A, 597, A90 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Euclid Collaboration (Knabenhans, M., et al.) 2021, MNRAS, 505, 2840 [NASA ADS] [CrossRef] [Google Scholar]
- Euclid Collaboration (Scaramella, R., et al.) 2022, A&A, 662, A112 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Gaia Collaboration (Prusti, T., et al.) 2016, A&A, 595, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Gaia Collaboration (Vallenari, A., et al.) 2023, A&A, 674, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Geurts, P., Ernst, D., & Wehenkel, L. 2006, Mach. Learn., 63, 3 [Google Scholar]
- Giusarma, E., Reyes Hurtado, M., Villaescusa-Navarro, F., et al. 2023, ApJ, 950, 11 [Google Scholar]
- Graham, M. J., Djorgovski, S. G., Drake, A. J., et al. 2014, MNRAS, 439, 703 [NASA ADS] [CrossRef] [Google Scholar]
- Hastie, T., Tibshirani, R., & Friedman, J. 2009, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Series in Statistics (Berlin: Springer) [Google Scholar]
- He, S., Li, Y., Feng, Y., et al. 2019, Proc. Natl. Acad. Sci., 116, 13825 [NASA ADS] [CrossRef] [Google Scholar]
- Ho, T. K. 1995, Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1), ICDAR ’95 (Washington: IEEE Computer Society), 278 [Google Scholar]
- Hotelling, H. 1933, J. Educ. Psych., 24, 417 [CrossRef] [Google Scholar]
- Huertas-Company, M., Rouan, D., Tasca, L., Soucail, G., & Le Fèvre, O. 2008, A&A, 478, 971 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Ishida, E. E. O., & de Souza, R. S. 2011, A&A, 527, A49 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Ivezić, Ž., Kahn, S. M., Tyson, J. A., et al. 2019, ApJ, 873, 111 [Google Scholar]
- Jamieson, D., Li, Y., Alves de Oliveira, R., et al. 2023, ApJ, 952, 145 [NASA ADS] [CrossRef] [Google Scholar]
- Jeffrey, W., & Rosner, R. 1986, ApJ, 310, 473 [NASA ADS] [CrossRef] [Google Scholar]
- Jollife, I. T. 2002, Principal Component Analysis (Belin: Springer-Verlag) [Google Scholar]
- Kamdar, H. M., Turk, M. J., & Brunner, R. J. 2016, MNRAS, 455, 642 [NASA ADS] [CrossRef] [Google Scholar]
- Kodi Ramanah, D., Charnock, T., Villaescusa-Navarro, F., & Wandelt, B. D. 2020, MNRAS, 495, 4227 [NASA ADS] [CrossRef] [Google Scholar]
- Koons, H. C., & Gorney, D. J. 1990, EOS Trans., 71, 677 [NASA ADS] [CrossRef] [Google Scholar]
- Krone-Martins, A., & Moitinho, A. 2014, A&A, 561, A57 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Krone-Martins, A., Ducourant, C., & Teixeira, R. 2008, in Classification and Discovery in Large Astronomical Surveys, ed. C. A. L. Bailer-Jones, AIP Conf. Ser., 1082, 151 [NASA ADS] [CrossRef] [Google Scholar]
- Krone-Martins, A., Delchambre, L., Wertz, O., et al. 2018, A&A, 616, L11 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Kuhn, M. 2008, J. Stat. Softw. Articles, 28, 1 [Google Scholar]
- Laureijs, R., Amiaux, J., Arduini, S., et al. 2011, ArXiv e-prints [arXiv:1110.3193] [Google Scholar]
- Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. 1998, Proc. IEEE, 86, 2278 [Google Scholar]
- Lucy, L. B. 1977, AJ, 82, 1013 [NASA ADS] [CrossRef] [Google Scholar]
- Mahabal, A., Djorgovski, S. G., Williams, R., et al. 2008, in Classification and Discovery in Large Astronomical Surveys, ed. C. A. L. Bailer-Jones, AIP Conf. Ser., 1082, 287 [NASA ADS] [CrossRef] [Google Scholar]
- McCulloch, W., & Pitts, W. 1943, Bull. Math. Biophys., 5, 115 [CrossRef] [Google Scholar]
- Mendes-Moreira, J., Soares, C., Jorge, A. M., & Sousa, J. F. D. 2012, ACM Comput. Surv., 45, 10 [CrossRef] [Google Scholar]
- Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., & Leisch, F. 2021, e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien, R package version 1.7-8 [Google Scholar]
- Monaghan, J. J. 1992, ARA&A, 30, 543 [NASA ADS] [CrossRef] [Google Scholar]
- Nun, I., Pichara, K., Protopapas, P., & Kim, D.-W. 2014, ApJ, 793, 23 [Google Scholar]
- Odewahn, S. C., Stockwell, E. B., Pennington, R. L., Humphreys, R. M., & Zumach, W. A. 1992, AJ, 103, 318 [NASA ADS] [CrossRef] [Google Scholar]
- O’Hagan, A., & Kingman, J. F. C. 1978, J. R. Stat. Soc. Ser. B (Methodol.), 40, 1 [Google Scholar]
- Pearson, K. 1901, Phil. Mag., 2, 559 [CrossRef] [Google Scholar]
- Perraudin, N., Srivastava, A., Lucchi, A., et al. 2019, Comput. Astrophys. Cosmol., 6, 5 [Google Scholar]
- Ramos, E. P. R. G., da Silva, A. J. C., & Liu, G.-C. 2012, ApJ, 757, 44 [NASA ADS] [CrossRef] [Google Scholar]
- R Core Team 2021, R: A Language and Environment for Statistical Computing (Vienna, Austria: R Foundation for Statistical Computing) [Google Scholar]
- Richards, J. W., Starr, D. L., Butler, N. R., et al. 2011, ApJ, 733, 10 [NASA ADS] [CrossRef] [Google Scholar]
- Rodríguez, A. C., Kacprzak, T., Lucchi, A., et al. 2018, Comput. Astrophys. Cosmol., 5, 4 [Google Scholar]
- Sandler, D. G., Barrett, T. K., Palmer, D. A., Fugate, R. Q., & Wild, W. J. 1991, Nature, 351, 300 [NASA ADS] [CrossRef] [Google Scholar]
- Sarro, L. M., Bouy, H., Berihuete, A., et al. 2014, A&A, 563, A45 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Scaringi, S., Bird, A. J., Clark, D. J., et al. 2008, in Classification and Discovery in Large Astronomical Surveys, ed. C. A. L. Bailer-Jones, AIP Conf. Ser., 1082, 307 [NASA ADS] [CrossRef] [Google Scholar]
- Sefusatti, E., Crocce, M., Pueblas, S., & Scoccimarro, R. 2006, Phys. Rev. D, 74, 023522 [NASA ADS] [CrossRef] [Google Scholar]
- Smith, K. W., Bailer-Jones, C. A. L., Klement, R. J., & Xue, X. X. 2010, A&A, 522, A88 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Steiner, J. E., Menezes, R. B., Ricci, T. V., & Oliveira, A. S. 2009, MNRAS, 395, 64 [NASA ADS] [CrossRef] [Google Scholar]
- Storrie-Lombardi, M. C., Lahav, O., Sodre, L., Jr., & Storrie-Lombardi, L., Jr. 1992, MNRAS, 259, 8P [NASA ADS] [CrossRef] [Google Scholar]
- Sugiyama, N. 1995, ApJS, 100, 281 [NASA ADS] [CrossRef] [Google Scholar]
- Tsalmantza, P., Kontizas, M., Bailer-Jones, C. A. L., et al. 2007, A&A, 470, 761 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Ullmo, M., Decelle, A., & Aghanim, N. 2021, A&A, 651, A46 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Veneri, M. D., de Souza, R. S., Krone-Martins, A., et al. 2022, Res. Notes Am. Astron. Soc., 6, 113 [Google Scholar]
- Wadadekar, Y. 2005, PASP, 117, 79 [NASA ADS] [CrossRef] [Google Scholar]
- Zhang, Y., Cui, C., & Zhao, Y. 2002, in Astronomical Data Analysis II, eds. J. L. Starck, & F. D. Murtagh, SPIE Conf. Ser., 4847, 371 [NASA ADS] [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.