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 09015
Number of page(s) 9
Section Artificial Intelligence and Machine Learning
DOI https://doi.org/10.1051/epjconf/202429509015
Published online 06 May 2024
  1. The CMS Collaboration et al 2008, JINST 3 S08004 [Google Scholar]
  2. The ATLAS Collaboration et al 2008, JINST 3 S08003 [Google Scholar]
  3. The ATLAS Collaboration et al, Phys.Lett.B 716 (2012) 1-29. [Google Scholar]
  4. The CMS Collaboration et al, Phys.Lett.B 716 (2012) 30-61. [Google Scholar]
  5. G. Apollinari, L. Rossi et al, Preliminary Design Report. CERN-2015-005 [Google Scholar]
  6. The CMS Collaboration et al, JINST 12 (2017) 10, P10003 [Google Scholar]
  7. M. Andrews, S. Gleyzer et al, Computing and Software for Big Science 4, 6(2020). [CrossRef] [Google Scholar]
  8. M. Andrews, S. Gleyzer et al., Nuclear Instruments and Methods A 977, 164304 (2020). [Google Scholar]
  9. M. Andrews, S. Gleyzer et al., Phys. Rev. D 105, 052008 (2022). [CrossRef] [Google Scholar]
  10. A. Hariri, D. Dyachkova, S. Gleyzer, arXiv:2104.01725, (2021). [Google Scholar]
  11. S. Qasim, et al, Eur.Phys.J.C 82 (2022)8, 753 [CrossRef] [Google Scholar]
  12. S Bhattacharya et al 2023 J. Phys.: Conf. Ser. 2438 012090. [Google Scholar]
  13. CMSSW, CMS software framework, https://github.com/cms-sw/cmssw [Google Scholar]
  14. T. Sjostrand et al, Comput.Phys.Commun. 178 (2008) 852-867. [CrossRef] [Google Scholar]
  15. S. Alioli et al, JHEP 06 (2010) 043. [Google Scholar]
  16. GEANT4 Collaboration, Nucl. Instrum. Meth. A 506(2003) 250 [Google Scholar]
  17. End-to-end framework, https://github.com/rchudasa/RecoE2E, Accessed: 20 December 2023. [Google Scholar]
  18. The CMS collaboration, Detector performance summary, CMS-DP-2023-036. [Google Scholar]
  19. S. HasanPour et al, Let’s keep it simple, Using simple architectures to outperform deeper and more complex architectures, arXiv:1608.06037. [Google Scholar]
  20. ONNX, Open Neural Network Exchange (ONNX), https://github.com/onnx/onnx, Accessed: 12 December 2023. [Google Scholar]
  21. M. Abadi et al., “TensorFlow: Large-scale machine learning on heterogeneous systems”, https://www.tensorflow.org/, Accessed: 12 December 2023. [Google Scholar]
  22. Pytorch, https://pytorch.org/, Accessed: 12 December 2023. [Google Scholar]
  23. T. Chen and C. Guestrin, Xgboost: A scalable tree boosting system, arXiv:1603.02754. [Google Scholar]
  24. Merkel, D. (2014). Docker: lightweight linux containers for consistent development and deployment. Linux Journal, 2014(239), 2. [Google Scholar]
  25. National Energy Research Scientific Computing Center, https://www.nersc.gov/. [Google Scholar]
  26. https://cvmfs.readthedocs.io/en/stable/, Accessed: 12 December 2023. [Google Scholar]
  27. Shifter: Linux Containers for HPC, https://github.com/NERSC/shifter, Accessed: 12 December 2023. [Google Scholar]
  28. NVIDIA Tesla P100, https://www.nvidia.com/en-us/data-center/tesla-p100/ [Google Scholar]
  29. NVIDIA A100, https://www.nvidia.com/en-us/data-center/a100/ [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.