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@INPROCEEDINGS{Buss:600425,
      author       = {Buss, Thorsten Lars Henrik and Diefenbacher, Sascha Daniel
                      and Gaede, Frank and Kasieczka, Gregor and Krause, Claudius
                      and Shih, David},
      title        = {{G}enerating {A}ccurate {S}howers in {H}ighly {G}ranular
                      {C}alorimeters {U}sing {C}onvolutional {N}ormalizing
                      {F}lows},
      school       = {DESY},
      reportid     = {PUBDB-2023-07982},
      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. It has been previously
                      demonstrated, that such flows can generate showers in
                      calorimeters with high accuracy. However, the main drawback
                      of normalizing flows with fully connected sub-networks is
                      that they scale poorly with input dimensions. We overcome
                      this issue by using a U-Net based flow architecture and show
                      how it can be applied to accurately simulate showers in
                      highly granular calorimeters.},
      month         = {Nov},
      date          = {2023-11-06},
      organization  = {Machine learning for jets, Hamburg
                       (Germany), 6 Nov 2023 - 10 Nov 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/600425},
}