Open Access
Issue
EPJ Web Conf.
Volume 337, 2025
27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)
Article Number 01233
Number of page(s) 12
DOI https://doi.org/10.1051/epjconf/202533701233
Published online 07 October 2025
  1. ATLAS Collaboration, Tech. rep., Technical report, CERN, Geneva. http://cds. cern. ch/record/2802918 (2022) [Google Scholar]
  2. S. Agostinelli, J. Allison, K.a. Amako, J. Apostolakis, H. Araujo, P. Arce, M. Asai, D. Axen, S. Banerjee, G. Barrand et al., Geant4—a simulation toolkit, Nuclear instruments and methods in physics research section A: Accelerators, Spectrometers, Detectors and Associated Equipment 506, 250 (2003). [Google Scholar]
  3. M. Paganini, L. de Oliveira, B. Nachman, Calogan: Simulating 3d high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks, Physical Review D 97, 014021 (2018). [Google Scholar]
  4. O. Amram, K. Pedro, Denoising diffusion models with geometry adaptation for high fidelity calorimeter simulation, Physical Review D 108, 072014 (2023). [Google Scholar]
  5. T. Buss, F. Gaede, G. Kasieczka, C. Krause, D. Shih, Convolutional l2lflows: generating accurate showers in highly granular calorimeters using convolutional normalizing flows, Journal of Instrumentation 19, P09003 (2024). 10.1088/1748-0221/19/09/P09003 [Google Scholar]
  6. C. Krause, M.F. Giannelli, G. Kasieczka, B. Nachman, D. Salamani, D. Shih, A. Zaborowska, O. Amram, K. Borras, M.R. Buckley et al., Calochallenge 2022: A community challenge for fast calorimeter simulation, arXiv preprint arXiv:2410.21611 (2024). [Google Scholar]
  7. G. Aad, B. Abbott, D.C. Abbott, A.A. Abud, K. Abeling, D.K. Abhayasinghe, S.H. Abidi, A. Aboulhorma, H. Abramowicz, H. Abreu et al., Atlfast3: the next generation of fast simulation in ATLAS, Computing and software for big science 6, 7 (2022). [Google Scholar]
  8. A. Abhishek, E. Drechsler, W. Fedorko, B. Stelzer, Calodvae : Discrete variational autoencoders for fast calorimeter shower simulation (2022), 2210.07430, https://arxiv.org/abs/2210.07430 [Google Scholar]
  9. S. Hoque, H. Jia, A. Abhishek, M. Fadaie, J.Q. Toledo-Marín, T. Vale, R.G. Melko, M. Swiatlowski, W.T. Fedorko, Caloqvae: Simulating high-energy particle-calorimeter interactions using hybrid quantum-classical generative models, The European Physical Journal C 84, 1 (2024). [Google Scholar]
  10. J.Q. Toledo-Marin, S. Gonzalez, H. Jia, I. Lu, D. Sogutlu, A. Abhishek, C. Gay, E. Paquet, R. Melko, G.C. Fox et al., Conditioned quantum-assisted deep generative surrogate for particle-calorimeter interactions (2024), 2410.22870. [Google Scholar]
  11. Michele Faucci Giannelli, Gregor Kasieczka, Claudius Krause, Ben Nachman, Dalila Salamani, David Shih, Anna Zaborowska, Fast calorimeter simulation challenge 2022 - dataset 1,2 and 3. zenodo., https://doi.org/10.5281/zenodo.8099322, https://doi.org/10.5281/zenodo.6366271, https://doi.org/10.5281/zenodo.6366324 (2022) [Google Scholar]
  12. C. Krause, D. Shih, Caloflow: fast and accurate generation of calorimeter showers with normalizing flows, arXiv preprint arXiv:2106.05285 (2021). [Google Scholar]
  13. V. Mikuni, B. Nachman, Caloscore v2: single-shot calorimeter shower simulation with diffusion models, Journal of Instrumentation 19, P02001 (2024). [Google Scholar]
  14. D-Wave Systems, Advantage processor overview, https://www.dwavesys.com/media/3xvdipcn/14-1058a-a_advantage_processor_overview.pdf (2022), accessed: 2023-11-07 [Google Scholar]
  15. D-Wave Systems, Advantage processor overview, https://www.dwavesys. com/media/2uznec4s/14-1056a-a_zephyr_topology_of_d-wave_quantum_ processors.pdf (2021), accessed: 2025-02-26 [Google Scholar]
  16. C.J. Maddison, A. Mnih, Y.W. Teh, The concrete distribution: A continuous relaxation of discrete random variables, arXiv preprint arXiv:1611.00712 (2016). [Google Scholar]
  17. K. Cho, T. Raiko, A.T. Ihler, Enhanced gradient and adaptive learning rate for training restricted Boltzmann machines, in Proceedings of the 28th international conference on machine learning (ICML-11) (Citeseer, 2011), pp. 105–112 [Google Scholar]
  18. J. Melchior, A. Fischer, L. Wiskott, How to center deep boltzmann machines, Journal of Machine Learning Research 17, 1 (2016). [Google Scholar]
  19. R. Kansal, A. Li, J. Duarte, N. Chernyavskaya, M. Pierini, B. Orzari, T. Tomei, Evaluating generative models in high energy physics, Physical Review D 107, 076017 (2023). [Google Scholar]
  20. L. Favaro, A. Ore, S.P. Schweitzer, T. Plehn, Calodream - detector response emulation via attentive flow matching, arXiv preprint arXiv:2405.09629 (2024). [Google Scholar]
  21. D-Wave Systems, Computational power consumption and speedup (2014), white paper, accessed: April 9, 2025, https://www.dwavesys.com/media/ivelyjij/14-1005a_d_wp_computational_power_consumption_and_speedup.pdf [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.