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@ARTICLE{Gaede:478310,
      author       = {Gaede, Frank and Eren, Engin and Krüger, Katja and
                      Diefenbacher, Sascha Daniel and Korol, Anatolii and
                      Kasieczka, Gregor and Buhmann, Erik},
      title        = {{G}etting {H}igh: {H}igh {F}idelity {S}imulation of {H}igh
                      {G}ranularity {C}alorimeters with {H}igh {S}peed},
      journal      = {Computing and software for big science},
      volume       = {5},
      number       = {1},
      issn         = {2510-2036},
      address      = {Cham, Switzerland},
      publisher    = {Springer International Publishing},
      reportid     = {PUBDB-2022-02526, arXiv:2005.05334. DESY-20-075},
      pages        = {13},
      year         = {2021},
      note         = {Computing and Software for Big Science 5, 13 (2021). 17
                      pages, 12 figures},
      abstract     = {Accurate simulation of physical processes is crucial for
                      the success of modern particle physics. However, simulating
                      the development and interaction of particle showers with
                      calorimeter detectors is a time consuming process and drives
                      the computing needs of large experiments at the LHC and
                      future colliders. Recently, generative machine learning
                      models based on deep neural networks have shown promise in
                      speeding up this task by several orders of magnitude. We
                      investigate the use of a new architecture—the Bounded
                      Information Bottleneck Autoencoder—for modelling
                      electromagnetic showers in the central region of the
                      Silicon-Tungsten calorimeter of the proposed International
                      Large Detector. Combined with a novel second post-processing
                      network, this approach achieves an accurate simulation of
                      differential distributions including for the first time the
                      shape of the minimum-ionizing-particle peak compared to a
                      full Geant4 simulation for a high-granularity calorimeter
                      with 27k simulated channels. The results are validated by
                      comparing to established architectures. Our results further
                      strengthen the case of using generative networks for fast
                      simulation and demonstrate that physically relevant
                      differential distributions can be described with high
                      accuracy.},
      keywords     = {calorimeter (INSPIRE) / iron (INSPIRE) / tungsten (INSPIRE)
                      / showers: electromagnetic (INSPIRE) / particle: interaction
                      (INSPIRE) / neural network (INSPIRE) / GEANT (INSPIRE) / ILD
                      detector (INSPIRE) / artificial intelligence (INSPIRE) /
                      numerical calculations (INSPIRE) / numerical methods
                      (INSPIRE) / performance (INSPIRE) / Deep learning (autogen)
                      / Generative models (autogen) / Calorimeter (autogen) /
                      Simulation (autogen) / High granularity (autogen) / GAN
                      (autogen) / WGAN (autogen) / BIB-AE (autogen)},
      cin          = {FLC},
      ddc          = {004},
      cid          = {I:(DE-H253)FLC-20120731},
      pnm          = {611 - Fundamental Particles and Forces (POF3-611)},
      pid          = {G:(DE-HGF)POF3-611},
      experiment   = {EXP:(DE-H253)ILC(machine)-20150101},
      typ          = {PUB:(DE-HGF)16},
      eprint       = {2005.05334},
      howpublished = {arXiv:2005.05334},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2005.05334;\%\%$},
      doi          = {10.1007/s41781-021-00056-0},
      url          = {https://bib-pubdb1.desy.de/record/478310},
}