Open Access
Issue |
A&A
Volume 677, September 2023
|
|
---|---|---|
Article Number | A16 | |
Number of page(s) | 19 | |
Section | Numerical methods and codes | |
DOI | https://doi.org/10.1051/0004-6361/202245189 | |
Published online | 28 August 2023 |
- Aguirre, C., Pichara, K., & Becker, I. 2018, MNRAS, 482, 5078 [Google Scholar]
- Alves, C. S., Peiris, H. V., Lochner, M., et al. 2022, ApJS, 258, 23 [NASA ADS] [CrossRef] [Google Scholar]
- Angus, R., Morton, T., Aigrain, S., Foreman-Mackey, D., & Rajpaul, V. 2017, MNRAS, 474, 2094 [Google Scholar]
- Ball, N. M., & Brunner, R. J. 2010, Int. J. Mod. Phys. D, 19, 1049 [Google Scholar]
- Baron, D. 2019, ArXiv e-prints [arXiv:1904.07248] [Google Scholar]
- Bassi, S., Sharma, K., & Gomekar, A. 2021, Front. Astron. Space Sci., 8, 168 [NASA ADS] [CrossRef] [Google Scholar]
- Bazin, G., Palanque-Delabrouille, N., Rich, J., et al. 2009, A&A, 499, 653 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Becker, I., Pichara, K., Catelan, M., et al. 2020, MNRAS, 493, 2981 [NASA ADS] [CrossRef] [Google Scholar]
- Bellm, E. C., Kulkarni, S. R., Barlow, T., et al. 2019, PASP, 131, 068003 [Google Scholar]
- Bishop, C. M. 2006, Pattern Recognition and Machine Learning (Information Science and Statistics) (Berlin, Heidelberg: Springer-Verlag) [Google Scholar]
- Blundell, C., Cornebise, J., Kavukcuoglu, K., & Wierstra, D. 2015, in Proceedings of the 32nd International Conference on Machine Learning (ICML 2015), 37 [Google Scholar]
- Boone, K. 2019, AJ, 158, 257 [NASA ADS] [CrossRef] [Google Scholar]
- Burhanudin, U. F., & Maund, J. R. 2022, MNRAS, 521, 1601 [Google Scholar]
- Dinh, L., Sohl-Dickstein, J., & Bengio, S. 2017, in International Conference on Learning Representations [Google Scholar]
- Dobryakov, S., Malanchev, K., Derkach, D., & Hushchyn, M. 2021, Astron. Comput., 35, 100451 [NASA ADS] [CrossRef] [Google Scholar]
- Drake, A. J., Djorgovski, S. G., Mahabal, A., et al. 2009, ApJ, 696, 870 [Google Scholar]
- Dubath, P., Rimoldini, L., Süveges, M., et al. 2011, MNRAS, 414, 2602 [Google Scholar]
- Ferreira Lopes, C. E., & Cross, N. J. G. 2017, A&A, 604, A121 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Förster, F., Cabrera-Vives, G., Castillo-Navarrete, E., et al. 2021, AJ, 161, 242 [CrossRef] [Google Scholar]
- Fremling, U. C., Miller, A. A., Sharma, Y., et al. 2020, ApJ, 895, 32 [NASA ADS] [CrossRef] [Google Scholar]
- Guy, J., Astier, P., Baumont, S., et al. 2007, A&A, 466, 11 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Hložek, R., Ponder, K. A., Malz, A. I., et al. 2020, ArXiv e-prints [arXiv:2012.12392] [Google Scholar]
- Ishida, E. E. O., Kornilov, M. V., Malanchev, K. L., et al. 2021, A&A, 650, A195 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Ivezić, Ž., Kahn, S. M., Tyson, J. A., et al. 2019, ApJ, 873, 111 [Google Scholar]
- James, G., Witten, D., Hastie, T., & Tibshirani, R. 2013, An Introduction to Statistical Learning: with Applications in R (Springer) [Google Scholar]
- Jones, D. O., Scolnic, D. M., Foley, R. J., et al. 2019, ApJ, 881, 19 [Google Scholar]
- Jones, D. O., Foley, R. J., Narayan, G., et al. 2021, ApJ, 908, 143 [NASA ADS] [CrossRef] [Google Scholar]
- Karpenka, N. V., Feroz, F., & Hobson, M. P. 2012, MNRAS, 429, 1278 [Google Scholar]
- Kessler, R., Bernstein, J. P., Cinabro, D., et al. 2009, PASP, 121, 1028 [Google Scholar]
- Kessler, R., Bassett, B., Belov, P., et al. 2010, PASP, 122, 1415 [CrossRef] [Google Scholar]
- Kim, A. G., Thomas, R. C., Aldering, G., et al. 2013, ApJ, 766, 84 [NASA ADS] [CrossRef] [Google Scholar]
- Kostenetskiy, P. S., Chulkevich, R. A., & Kozyrev, V. I. 2021, J. Phys. Conf. Ser., 1740, 012050 [NASA ADS] [CrossRef] [Google Scholar]
- Lipunov, V., Kornilov, V., Gorbovskoy, E., et al. 2010, Adv. Astron., 2010, 349171 [Google Scholar]
- Lochner, M., McEwen, J. D., Peiris, H. V., Lahav, O., & Winter, M. K. 2016, Astrophys. J. Suppl. Ser., 225, 31 [NASA ADS] [CrossRef] [Google Scholar]
- Mahabal, A., Sheth, K., Gieseke, F., et al. 2017, in 2017 IEEE Symp. Ser. Comput. Intell. (SSCI), 1-8 [Google Scholar]
- Matheson, T., Stubens, C., Wolf, N., et al. 2021, AJ, 161, 107 [NASA ADS] [CrossRef] [Google Scholar]
- Möller, A., & de Boissière, T. 2020, MNRAS, 491, 4277 [CrossRef] [Google Scholar]
- Möller, A., Peloton, J., Ishida, E. E. O., et al. 2021, MNRAS, 501, 3272 [CrossRef] [Google Scholar]
- Müller-Bravo, T. E., Sullivan, M., Smith, M., et al. 2022, MNRAS, 512, 3266 [CrossRef] [Google Scholar]
- Muthukrishna, D., Mandel, K. S., Lochner, M., Webb, S., & Narayan, G. 2022, MNRAS, 517, 393 [NASA ADS] [CrossRef] [Google Scholar]
- Naul, B., Bloom, J. S., Pérez, F., & van der Walt, S. 2017, Nat. Astron., 2, 151 [NASA ADS] [CrossRef] [Google Scholar]
- Newling, J., Varughese, M., Bassett, B., et al. 2011, MNRAS, 414, 1987 [NASA ADS] [CrossRef] [Google Scholar]
- Pashchenko, I. N., Sokolovsky, K. V., & Gavras, P. 2017, MNRAS, 475, 2326 [Google Scholar]
- Paszke, A., Gross, S., Massa, F., et al. 2019, in Advances in Neural Informa- tion Processing Systems 32, eds. H. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché-Buc, E. Fox, & R. Garnett (Curran Associates, Inc.), 8024 [Google Scholar]
- Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, J. Mach. Learn. Res., 12, 2825 [Google Scholar]
- Perlmutter, S., Aldering, G., Goldhaber, G., et al. 1999, ApJ, 517, 565 [Google Scholar]
- Phillips, M. M. 1993, ApJ, 413, L105 [Google Scholar]
- Pojmanski, G. 1997, Acta Astron., 47, 467 [Google Scholar]
- Pruzhinskaya, M. V., Malanchev, K. L., Kornilov, M. V., et al. 2019, MNRAS, 489, 3591 [Google Scholar]
- Pskovskii, I. P. 1977, Soviet Ast., 21, 675 [Google Scholar]
- Qu, H., Sako, M., Möller, A., & Doux, C. 2021, AJ, 162, 67 [NASA ADS] [CrossRef] [Google Scholar]
- Quiñonero-Candela, J., Rasmussen, C., Sinz, F., Bousquet, O., & Schölkopf, B. 2006, in Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment (Springer), 3944, 1 [CrossRef] [Google Scholar]
- Rezende, D., & Mohamed, S. 2015, in Proceedings of the 32nd International Conference on Machine Learning, eds. F. Bach, & D. Blei (Lille, France: PMLR), 37, 1530 [Google Scholar]
- Richards, J. W., Starr, D. L., Butler, N. R., et al. 2011, ApJ, 733, 10 [NASA ADS] [CrossRef] [Google Scholar]
- Riess, A. G., Press, W. H., & Kirshner, R. P. 1996, ApJ, 473, 88 [Google Scholar]
- Riess, A. G., Filippenko, A. V., Challis, P., et al. 1998, AJ, 116, 1009 [Google Scholar]
- Rust, B. W. 1974, Ph.D. Thesis, Oak Ridge National Laboratory, Tennessee, USA [Google Scholar]
- Sánchez-Sáez, P., Reyes, I., Valenzuela, C., et al. 2021, AJ, 161, 141 [CrossRef] [Google Scholar]
- Shrestha, D. L., & Solomatine, D. P. 2006, Neural Networks, 19, 225 [CrossRef] [Google Scholar]
- Sravan, N., Graham, M. J., Fremling, C., & Coughlin, M. W. 2022, in Big-Data-Analytics in Astronomy, Science, and Engineering, eds. S. Sachdeva, Y. Watanobe, & S. Bhalla (Cham: Springer International Publishing), 59 [CrossRef] [Google Scholar]
- Stevance, H. F., & Lee, A. 2022, MNRAS, 518, 5741 [CrossRef] [Google Scholar]
- Tabak, E. G., & Turner, C. V. 2013, Commun. Pure Appl. Math., 66, 145 [CrossRef] [Google Scholar]
- Taddia, F., Sollerman, J., Leloudas, G., et al. 2015, A&A, 574, A60 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- The PLAsTiCC Team (Allam, T., et al.) 2018, ArXiv e-prints [arXiv: 1810.00001] [Google Scholar]
- Tonry, J. L., Denneau, L., Heinze, A. N., et al. 2018, PASP, 130, 064505 [Google Scholar]
- Villar, V. A., Berger, E., Miller, G., et al. 2019, ApJ, 884, 83 [NASA ADS] [CrossRef] [Google Scholar]
- Villar, V. A., Cranmer, M., Berger, E., et al. 2021, ApJS, 255, 24 [NASA ADS] [CrossRef] [Google Scholar]
- Williams, C., & Rasmussen, C. 1995, in Advances in Neural Information Processing Systems 8 (NIPS 1995), eds. D. Touretzky, M.C. Mozer, & M. Hasselm (MIT Press) [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.