Open Access
Issue |
A&A
Volume 698, May 2025
|
|
---|---|---|
Article Number | A3 | |
Number of page(s) | 14 | |
Section | Cosmology (including clusters of galaxies) | |
DOI | https://doi.org/10.1051/0004-6361/202453284 | |
Published online | 23 May 2025 |
- Abadi, M., Agarwal, A., Barham, P., et al. 2015, TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, https://www.tensorflow.org [Google Scholar]
- Akiba, T., Sano, S., Yanase, T., Ohta, T., & Koyama, M. 2019, ArXiv e-prints [arXiv:1907.10902] [Google Scholar]
- Arjovsky, M., Chintala, S., & Bottou, L. 2017, ArXiv e-prints [arXiv:1701.07875] [Google Scholar]
- Artale, M. C., Zehavi, I., Contreras, S., & Norberg, P. 2018, MNRAS, 480, 3978 [NASA ADS] [CrossRef] [Google Scholar]
- Bergstra, J., Bardenet, R., Bengio, Y., & Kégl, B. 2011, in Advances in Neural Information Processing Systems, eds. J. Shawe-Taylor, R. Zemel, P. Bartlett, F. Pereira, & K. Weinberger (Curran Associates, Inc.), 24 [Google Scholar]
- Bingham, E., Chen, J. P., Jankowiak, M., et al. 2019, J. Mach. Learn. Res., 20, 28:1 [Google Scholar]
- Bishop, C. 1994, Mixture Density Networks, Workingpaper, Aston University, USA [Google Scholar]
- Bose, S., Eisenstein, D. J., Hernquist, L., et al. 2019, MNRAS, 490, 5693 [CrossRef] [Google Scholar]
- Branco, P., Torgo, L., & Ribeiro, R. P. 2017, Proc. Mach. Learn. Res., 74, 36 [Google Scholar]
- Bullock, J. S., Dekel, A., Kolatt, T. S., et al. 2001, ApJ, 555, 240 [NASA ADS] [CrossRef] [Google Scholar]
- Buser, R. 1978, A&A, 62, 411 [NASA ADS] [Google Scholar]
- Chuang, C.-Y., Jespersen, C. K., Lin, Y.-T., Ho, S., & Genel, S. 2024, ApJ, 965, 101 [Google Scholar]
- Coccaro, A., Letizia, M., Reyes-González, H., & Torre, R. 2024, Symmetry, 16, 942 [Google Scholar]
- Contreras, S., Angulo, R. E., & Zennaro, M. 2021, MNRAS, 504, 5205 [CrossRef] [Google Scholar]
- de Santi, N. S. M., Rodrigues, N. V. N., Montero-Dorta, A. D., et al. 2022, MNRAS, 514, 2463 [CrossRef] [Google Scholar]
- Dinh, L., Krueger, D., & Bengio, Y. 2014, ArXiv e-prints [arXiv:1410.8516] [Google Scholar]
- Dinh, L., Sohl-Dickstein, J., & Bengio, S. 2016, ArXiv e-prints [arXiv:1605.08803] [Google Scholar]
- Dolatabadi, H. M., Erfani, S., & Leckie, C. 2020, ArXiv e-prints [arXiv:2001.05168] [Google Scholar]
- Durkan, C., Bekasov, A., Murray, I., & Papamakarios, G. 2019, ArXiv e-prints [arXiv:1906.04032] [Google Scholar]
- Fasano, G., & Franceschini, A. 1987, MNRAS, 225, 155 [NASA ADS] [CrossRef] [Google Scholar]
- Favole, G., Montero-Dorta, A. D., Artale, M. C., et al. 2021, MNRAS, 509, 1614 [CrossRef] [Google Scholar]
- Flamary, R., Courty, N., Gramfort, A., et al. 2021, J. Mach. Learn. Res., 22, 1 [Google Scholar]
- Gebhardt, M., Anglés-Alcázar, D., Borrow, J., et al. 2024, MNRAS, 529, 4896 [NASA ADS] [CrossRef] [Google Scholar]
- Genel, S., Vogelsberger, M., Springel, V., et al. 2014, MNRAS, 445, 175 [Google Scholar]
- Genel, S., Bryan, G. L., Springel, V., et al. 2019, ApJ, 871, 21 [NASA ADS] [CrossRef] [Google Scholar]
- Gu, M., Conroy, C., Diemer, B., et al. 2020, ArXiv e-prints [arXiv:2010.04166] [Google Scholar]
- Guo, Q., White, S., Angulo, R., et al. 2013, MNRAS, 428, 1351 [NASA ADS] [CrossRef] [Google Scholar]
- Hadzhiyska, B., Bose, S., Eisenstein, D., Hernquist, L., & Spergel, D. N. 2020, MNRAS, 493, 5506 [NASA ADS] [CrossRef] [Google Scholar]
- Hadzhiyska, B., Bose, S., Eisenstein, D., & Hernquist, L. 2021, MNRAS, 501, 1603 [Google Scholar]
- Ivezić, Ž., Connolly, A., VanderPlas, J., & Gray, A. 2014, Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data, Princeton Series in Modern Observational Astronomy (Princeton University Press) [Google Scholar]
- Jespersen, C. K., Cranmer, M., Melchior, P., et al. 2022, ApJ, 941, 7 [NASA ADS] [CrossRef] [Google Scholar]
- Jo, Y., & Kim, J.-H. 2019, MNRAS, 489, 3565 [Google Scholar]
- Kingma, D. P., & Ba, J. 2014, ArXiv e-prints [arXiv:1412.6980] [Google Scholar]
- Kingma, D. P., Salimans, T., Jozefowicz, R., et al. 2016, ArXiv e-prints [arXiv:1606.04934] [Google Scholar]
- Kunz, N. 2019, SMOGN, https://github.com/nickkunz/smogn [Google Scholar]
- Lemos, P., Coogan, A., Hezaveh, Y., & Perreault-Levasseur, L. 2023, ArXiv e-prints [arXiv:2302.03026] [Google Scholar]
- Lima, E., Sodré, L., Bom, C., et al. 2022, Astron. Comput., 38, 100510 [NASA ADS] [CrossRef] [Google Scholar]
- Lovell, C. C., Wilkins, S. M., Thomas, P. A., et al. 2021, MNRAS, 509, 5046 [Google Scholar]
- Lovell, C. C., Hassan, S., Villaescusa-Navarro, F., et al. 2023, Machine Learning for Astrophysics, 21 [Google Scholar]
- Marinacci, F., Vogelsberger, M., Pakmor, R., et al. 2018, MNRAS, 480, 5113 [NASA ADS] [Google Scholar]
- Montero-Dorta, A. D., & Rodriguez, F. 2024, MNRAS, 531, 290 [NASA ADS] [CrossRef] [Google Scholar]
- Montero-Dorta, A. D., Artale, M. C., Abramo, L. R., et al. 2020, MNRAS, 496, 1182 [NASA ADS] [CrossRef] [Google Scholar]
- Montero-Dorta, A. D., Artale, M. C., Abramo, L. R., & Tucci, B. 2021a, MNRAS, 504, 4568 [Google Scholar]
- Montero-Dorta, A. D., Chaves-Montero, J., Artale, M. C., & Favole, G. 2021b, MNRAS, 508, 940 [NASA ADS] [CrossRef] [Google Scholar]
- Naiman, J. P., Pillepich, A., Springel, V., et al. 2018, MNRAS, 477, 1206 [Google Scholar]
- Navarro, J. F., Frenk, C. S., & White, S. D. M. 1997, ApJ, 490, 493 [Google Scholar]
- Nelson, D., Pillepich, A., Springel, V., et al. 2018, MNRAS, 475, 624 [Google Scholar]
- Nelson, D., Springel, V., Pillepich, A., et al. 2019, Comput. Astrophys. Cosmol., 6, 2 [Google Scholar]
- Neyrinck, M. C. 2008, MNRAS, 386, 2101 [CrossRef] [Google Scholar]
- Ortega-Martinez, S., Contreras, S., & Angulo, R. 2024, A&A, 689, A66 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Papamakarios, G., Nalisnick, E., Rezende, D. J., Mohamed, S., & Lakshminarayanan, B. 2021, J. Mach. Learn. Res., 22, 1 [Google Scholar]
- Peacock, J. A. 1983, MNRAS, 202, 615 [NASA ADS] [Google Scholar]
- Pillepich, A., Nelson, D., Hernquist, L., et al. 2018a, MNRAS, 475, 648 [Google Scholar]
- Pillepich, A., Springel, V., Nelson, D., et al. 2018b, MNRAS, 473, 4077 [Google Scholar]
- Planck Collaboration XIII. 2016, A&A, 594, A13 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Ramdas, A., Garcia, N., & Cuturi, M. 2015, ArXiv e-prints [arXiv:1509.02237] [Google Scholar]
- Rodrigues, N. V. N., de Santi, N. S. M., Montero-Dorta, A. D., & Abramo, L. R. 2023, MNRAS, 522, 3236 [NASA ADS] [CrossRef] [Google Scholar]
- Shi, J., Wang, H., Mo, H., et al. 2020, ApJ, 893, 139 [Google Scholar]
- Springel, V. 2010, MNRAS, 401, 791 [Google Scholar]
- Springel, V., Pakmor, R., Pillepich, A., et al. 2018, MNRAS, 475, 676 [Google Scholar]
- Stiskalek, R., Bartlett, D. J., Desmond, H., & Anbajagane, D. 2022, MNRAS, 514, 4026 [Google Scholar]
- Taillon, G. 2018, 2DKS, https://github.com/Gabinou/2DKS [Google Scholar]
- Talts, S., Betancourt, M., Simpson, D., Vehtari, A., & Gelman, A. 2020, ArXiv e-prints [arXiv:1804.06788] [Google Scholar]
- Villaescusa-Navarro, F., Anglés-Alcázar, D., Genel, S., et al. 2021, ApJ, 915, 71 [NASA ADS] [CrossRef] [Google Scholar]
- Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nat. Meth., 17, 261 [Google Scholar]
- Vogelsberger, M., Genel, S., Springel, V., et al. 2014a, MNRAS, 444, 1518 [Google Scholar]
- Vogelsberger, M., Genel, S., Springel, V., et al. 2014b, Nature, 509, 177 [Google Scholar]
- Wechsler, R. H., & Tinker, J. L. 2018, ARA&A, 56, 435 [NASA ADS] [CrossRef] [Google Scholar]
- White, S. D. M., & Frenk, C. S. 1991, ApJ, 379, 52 [Google Scholar]
- Wu, J. F., Jespersen, C. K., & Wechsler, R. H. 2024, ApJ, 976, 37 [Google Scholar]
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