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@ARTICLE{Bieringer:480166,
      author       = {Bieringer, S. and Butter, A. and Diefenbacher, S. and Eren,
                      E. and Gaede, F. and Hundhausen, D. and Kasieczka, G. and
                      Nachman, B. and Plehn, T. and Trabs, M.},
      title        = {{C}alomplification — the power of generative calorimeter
                      models},
      journal      = {Journal of Instrumentation},
      volume       = {17},
      number       = {09},
      issn         = {1748-0221},
      address      = {London},
      publisher    = {Inst. of Physics},
      reportid     = {PUBDB-2022-03543, DESY-22-111. arXiv:2202.07352},
      pages        = {P09028},
      year         = {2022},
      note         = {17 pages, 10 figures},
      abstract     = {Motivated by the high computational costs of classical
                      simulations, machine-learned generative models can be
                      extremely useful in particle physics and elsewhere. They
                      become especially attractive when surrogate models can
                      efficiently learn the underlying distribution, such that a
                      generated sample outperforms a training sample of limited
                      size. This kind of GANplification has been observed for
                      simple Gaussian models. We show the same effect for a
                      physics simulation, specifically photon showers in an
                      electromagnetic calorimeter.},
      keywords     = {photon: showers (INSPIRE) / calorimeter: electromagnetic
                      (INSPIRE) / costs (INSPIRE) / Detector modelling and
                      simulations I (interaction of radiation with matter
                      (autogen) / interaction of photons with matter (autogen) /
                      interaction of hadrons with matter (autogen) / etc)
                      (autogen) / Simulation methods and programs (autogen) /
                      Analysis and statistical methods (autogen) / Calorimeter
                      methods (autogen)},
      cin          = {FTX},
      ddc          = {610},
      cid          = {I:(DE-H253)FTX-20210408},
      pnm          = {611 - Fundamental Particles and Forces (POF4-611) /
                      HIDSS-0002 - DASHH: Data Science in Hamburg - Helmholtz
                      Graduate School for the Structure of Matter
                      $(2019_IVF-HIDSS-0002)$ / DFG project 396021762 - TRR 257:
                      Phänomenologische Elementarteilchenphysik nach der
                      Higgs-Entdeckung (396021762) / DFG project 390900948 - EXC
                      2181: STRUKTUREN: Emergenz in Natur, Mathematik und
                      komplexen Daten (390900948) / DFG project 390833306 - EXC
                      2121: Quantum Universe (390833306)},
      pid          = {G:(DE-HGF)POF4-611 / $G:(DE-HGF)2019_IVF-HIDSS-0002$ /
                      G:(GEPRIS)396021762 / G:(GEPRIS)390900948 /
                      G:(GEPRIS)390833306},
      experiment   = {EXP:(DE-MLZ)NOSPEC-20140101},
      typ          = {PUB:(DE-HGF)16},
      eprint       = {2202.07352},
      howpublished = {arXiv:2202.07352},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2202.07352;\%\%$},
      UT           = {WOS:000888844600007},
      doi          = {10.1088/1748-0221/17/09/P09028},
      url          = {https://bib-pubdb1.desy.de/record/480166},
}