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All issues Volume 295 (2024) EPJ Web of Conf., 295 (2024) 09022 References
Open Access
Issue
EPJ Web of Conf.
Volume 295, 2024
26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023)
Article Number 09022
Number of page(s) 9
Section Artificial Intelligence and Machine Learning
DOI https://doi.org/10.1051/epjconf/202429509022
Published online 06 May 2024
  1. P. Shanahan et al. (2022), 2209.07559 [Google Scholar]
  2. G. Karagiorgi, G. Kasieczka, S. Kravitz, B. Nachman, D. Shih, Nature Rev. Phys. 4, 399 (2022) [CrossRef] [Google Scholar]
  3. A. Radovic, M. Williams, D. Rousseau, M. Kagan, D. Bonacorsi, A. Himmel, A. Aurisano, K. Terao, T. Wongjirad, Nature 560, 41 (2018) [CrossRef] [PubMed] [Google Scholar]
  4. C. Grojean, A. Paul, Z. Qian, I. Strümke, Nature Rev. Phys. 4, 284 (2022), 2203.08021 [CrossRef] [Google Scholar]
  5. E. Aprile et al. (XENON), Phys. Rev. D 108, 012016 (2023), 2304.05428 [CrossRef] [Google Scholar]
  6. N. Shaheed, X. Chen, M. Wang, JINST 18, T06002 (2023), 2303.05088 [CrossRef] [Google Scholar]
  7. P. Brás, F. Neves, A. Lindote, A. Cottle, R. Cabrita, E. Lopez Asamar, G. Pereira, C. Silva, V. Solovov, M.I. Lopes, Eur. Phys. J. C 82, 553 (2022), 2201.05659 [CrossRef] [Google Scholar]
  8. M. Biassoni, A. Giachero, M. Grossi, D. Guffanti, D. Labranca, R. Moretti, M. Rossi, F. Terranova, S. Vallecorsa (2023), 2305.09744 [Google Scholar]
  9. J. Aalbers et al. (LZ), Phys. Rev. Lett. 131, 041002 (2023), 2207.03764 [CrossRef] [PubMed] [Google Scholar]
  10. Y. Meng et al. (PandaX-4T), Phys. Rev. Lett. 127, 261802 (2021), 2107.13438 [CrossRef] [PubMed] [Google Scholar]
  11. E. Aprile et al. (XENON), Phys. Rev. Lett. 131, 041003 (2023), 2303.14729 [CrossRef] [PubMed] [Google Scholar]
  12. E. Aprile et al. (XENON), Phys. Rev. C 106, 024328 (2022), 2205.04158 [CrossRef] [Google Scholar]
  13. E. Aprile et al. (XENON), JCAP 11, 031 (2020), 2007.08796 [CrossRef] [Google Scholar]
  14. E. Aprile et al. (XENON), Phys. Rev. Lett. 129, 161805 (2022), 2207.11330 [CrossRef] [PubMed] [Google Scholar]
  15. L. Althüser, Light collection efficiency simulations of the xenon1t experiment and comparison to data (2017) [Google Scholar]
  16. M. Szydagis, N. Barry, K. Kazkaz, J. Mock, D. Stolp, M. Sweany, M. Tripathi, S. Uvarov, N. Walsh, M. Woods, JINST 6, P10002 (2011), 1106.1613 [CrossRef] [Google Scholar]
  17. P. Gaemers, T. Zhu, J.R. Angevaare, D.R. García, K. Mizukoshi, J. Aalbers, A. Terliuk, H.S. Eißing, L. Althüser, E. Shockley et al., Xenonnt/wfsim: v0.6.1 (2022), https://doi.org/10.5281/zenodo.6452974 [Google Scholar]
  18. M. Szydagis, S. Andaloro, J. Balajthy, G. Block, J. Brodsky, J. Cutter, J. Huang, E. Kozlova, B. Lenardo, A. Manalaysay et al., Noble element simulation technique (2021), https://doi.org/10.5281/zenodo.4569211 [Google Scholar]
  19. M.A. Kramer, AIChE Journal 37, 233 (1991), https://aiche.onlinelibrary.wiley.com/doi/pdf/10.1002/aic.690370209 [CrossRef] [Google Scholar]
  20. O. Atkinson, A. Bhardwaj, C. Englert, V.S. Ngairangbam, M. Spannowsky, JHEP 08, 080 (2021), 2105.07988 [CrossRef] [Google Scholar]
  21. L. Anzalone, S.S. Chhibra, B. Maier, N. Chernyavskaya, M. Pierini (2023), 2306.12955 [Google Scholar]
  22. G.D. Guglielmo, F. Fahim, C. Herwig, M.B. Valentin, J. Duarte, C. Gingu, P. Harris, J. Hirschauer, M. Kwok, V. Loncar et al., IEEE Transactions on Nuclear Science 68, 2179 (2021) [CrossRef] [Google Scholar]
  23. J. Herrero-Garcia, R. Patrick, A. Scaffidi, JCAP 02, 039 (2022), 2110.12248 [CrossRef] [Google Scholar]
  24. M. Kuusela, T. Vatanen, E. Malmi, T. Raiko, T. Aaltonen, Y. Nagai, J. Phys. Conf. Ser. 368, 012032 (2012), 1112.3329 [CrossRef] [Google Scholar]
  25. W. Xu, H. Sun, C. Deng, Y. Tan, Proceedings of the AAAI Conference on Artificial Intelligence 31 (2017) [Google Scholar]
  26. K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition (2015), 1512.03385 [Google Scholar]
  27. D.P. Kingma, M. Welling, Auto-encoding variational bayes (2022), 1312.6114 [Google Scholar]
  28. A. Hariri, D. Dyachkova, S. Gleyzer (2021), 2104.01725 [Google Scholar]
  29. J.C. Cresswell, B.L. Ross, G. Loaiza-Ganem, H. Reyes-Gonzalez, M. Letizia, A.L. Caterini, CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds, in 36th Conference on Neural Information Processing Systems (2022), 2211.15380 [Google Scholar]
  30. Python Software Foundation, Python language reference, version 3.8.18, http://www. python.org [Google Scholar]
  31. J.D. Hunter, Computing in Science & Engineering 9, 90 (2007) [Google Scholar]
  32. C.R. Harris et al., Nature 585, 357 (2020) [NASA ADS] [CrossRef] [Google Scholar]
  33. The pandas development team, pandas-dev/pandas: Pandas (2023), https://doi. org/10.5281/zenodo.8364959 [Google Scholar]
  34. A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga et al., Pytorch: An imperative style, high-performance deep learning library (2019), 1912.01703 [Google Scholar]

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