Open Access
Issue
EPJ Web Conf.
Volume 245, 2020
24th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019)
Article Number 02035
Number of page(s) 7
Section 2 - Offline Computing
DOI https://doi.org/10.1051/epjconf/202024502035
Published online 16 November 2020
  1. ATLAS Collaboration, JINST 3, S08003 (2008) [Google Scholar]
  2. ATLAS Collaboration, ATLAS Insertable B-Layer Technical Design Report, ATLAS-TDR-19 (2010), https://cds.cern.ch/record/1291633 [Google Scholar]
  3. B. Abbott et al., JINST 13, T05008 (2018), 1803.00844 [CrossRef] [Google Scholar]
  4. ATLAS Collaboration, Eur. Phys. J. C 70, 823 (2010), 1005.4568 [CrossRef] [EDP Sciences] [Google Scholar]
  5. S. Agostinelli et al. (GEANT4), Nucl. Instrum. Meth. A 506, 250 (2003) [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  6. ATLAS Collaboration, Computing and Software Public Results, https://twiki.cern.ch/twiki/bin/view/AtlasPublic/ComputingandSoftwarePublicResults [Google Scholar]
  7. ATLAS Collaboration, The new Fast Calorimeter Simulation in ATLAS, ATL-SOFT-PUB-2018-002 (2018), https://cds.cern.ch/record/2630434 [Google Scholar]
  8. ATLAS Collaboration, The simulation principle and performance of the ATLAS fast calorimeter simulation FastCaloSim, ATL-PHYS-PUB-2010-013 (2010), https://cds.cern.ch/record/1300517 [Google Scholar]
  9. R. Brun, F. Rademakers, Nucl. Instrum. Meth. A 389, 81 (1997) [NASA ADS] [CrossRef] [Google Scholar]
  10. ATLAS Collaboration, Plots on Fast Simulation for NEC, https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PLOTS/SIM-2019-006/ [Google Scholar]
  11. S.J. Gasiorowski (ATLAS Collaboration), Tech. Rep. ATL-SOFT-PROC-2020-027, CERN, Geneva (2020), https://cds.cern.ch/record/2712930 [Google Scholar]
  12. I.J. Goodfellow et al., Generative Adversarial Networks (2014), 1406.2661 [Google Scholar]
  13. D.J. Rezende, S. Mohamed, D. Wierstra, Stochastic backpropagation and approximate inference in deep generative models (2014), 1401.4082 [Google Scholar]
  14. D.P. Kingma, M. Welling, Auto-Encoding Variational Bayes (2014), 1312.6114 [Google Scholar]
  15. ATLAS Collaboration, Deep generative models for fast shower simulation in ATLAS, ATL-SOFT-PUB-2018-001 (2018), https://cds.cern.ch/record/2630433 [Google Scholar]
  16. ATLAS Collaboration, Energy resolution with a Generative Adversarial Network for Fast Shower Simulation in ATLAS, https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PLOTS/SIM-2019-004/ [Google Scholar]
  17. ATLAS Collaboration, VAE for photon shower simulation in ATLAS, https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PLOTS/SIM-2019-007/ [Google Scholar]
  18. A. Basalaev, Z. Marshall (ATLAS), J. Phys. Conf. Ser. 898, 042016 (2017) [Google Scholar]
  19. K. Edmonds et al., The fast ATLAS track simulation (FATRAS), ATL-SOFT-PUB-2008001 (2008), https://cds.cern.ch/record/1091969 [Google Scholar]
  20. S. Hamilton et al., The ATLAS Fast Track Simulation project (FATRAS), in IEEE Nuclear Science Symposuim Medical Imaging Conference (2010), pp. 311–316 [Google Scholar]
  21. J. Mechnich et al., J. Phys. Conf. Ser. 331, 032046 (2011) [Google Scholar]
  22. ATLAS Collaboration, ATLAS Simulation CPU Performance, https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PLOTS/SIM-2019-002/ [Google Scholar]

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