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@INPROCEEDINGS{Buss:600423,
      author       = {Buss, Thorsten Lars Henrik and Diefenbacher, Sascha Daniel
                      and Gaede, Frank and Kasieczka, Gregor and Krause, Claudius
                      and Shih, David and Eren, Engin and Shekhzadeh, Imahn},
      title        = {{G}enerating {A}ccurate {S}howers in {H}ighly {G}ranular
                      {C}alorimeters {U}sing {N}ormalizing {F}lows},
      school       = {Universitaet Hamburg},
      reportid     = {PUBDB-2023-07980},
      year         = {2023},
      abstract     = {The full simulation of particle colliders incurs a
                      significant computational cost. Among the most
                      resource-intensive steps are detector simulations. It is
                      expected that future developments, such as higher collider
                      luminosities and highly granular calorimeters, will increase
                      the computational resource requirement for simulation beyond
                      availability. One possible solution is generative neural
                      networks that can accelerate simulations. Normalizing flows
                      are a promising approach in this pursuit. It has been
                      previously demonstrated, that such flows can generate
                      showers in low-complexity calorimeters with high accuracy.
                      We show how normalizing flows can be improved and adapted
                      for precise shower simulation in significantly more complex
                      calorimeter geometries.},
      month         = {May},
      date          = {2023-05-08},
      organization  = {26th International Conference on
                       Computing in High Energy $\&$ Nuclear
                       Physics, Norfolk (USA), 8 May 2023 - 12
                       May 2023},
      subtyp        = {After Call},
      cin          = {UNI/EXP / FTX},
      cid          = {$I:(DE-H253)UNI_EXP-20120731$ / I:(DE-H253)FTX-20210408},
      pnm          = {623 - Data Management and Analysis (POF4-623) / 05D23GU4 -
                      Verbundprojekt 05D2022 - KISS: Künstliche Intelligenz zur
                      schnellen Simulation von wissenschaftlichen Daten.
                      Teilprojekt 1. (BMBF-05D23GU4) / DFG project 390833306 - EXC
                      2121: Quantum Universe (390833306)},
      pid          = {G:(DE-HGF)POF4-623 / G:(DE-Ds200)BMBF-05D23GU4 /
                      G:(GEPRIS)390833306},
      experiment   = {EXP:(DE-MLZ)NOSPEC-20140101},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://bib-pubdb1.desy.de/record/600423},
}