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@INPROCEEDINGS{Buss:600426,
      author       = {Buss, Thorsten Lars Henrik and Diefenbacher, Sascha Daniel
                      and Eren, Engin and Gaede, Frank and Kasieczka, Gregor and
                      Krause, Claudius and Shekhzadeh, Imahn and Shih, David},
      title        = {{G}enerating {A}ccurate {S}howers in {H}ighly {G}ranular
                      {C}alorimeters {U}sing {N}ormalizing {F}lows},
      reportid     = {PUBDB-2023-07983},
      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         = {Mar},
      date          = {2023-03-20},
      organization  = {DPG Frühjahrstagung, Dresden
                       (Germany), 20 Mar 2023 - 24 Mar 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) / DFG project
                      390833306 - EXC 2121: Quantum Universe (390833306) /
                      05D23GU4 - Verbundprojekt 05D2022 - KISS: Künstliche
                      Intelligenz zur schnellen Simulation von wissenschaftlichen
                      Daten. Teilprojekt 1. (BMBF-05D23GU4)},
      pid          = {G:(DE-HGF)POF4-623 / G:(GEPRIS)390833306 /
                      G:(DE-Ds200)BMBF-05D23GU4},
      experiment   = {EXP:(DE-MLZ)NOSPEC-20140101},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://bib-pubdb1.desy.de/record/600426},
}