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@INPROCEEDINGS{Buhmann:600313,
      author       = {Buhmann, Erik and Engin, Eren and Gaede, Frank and
                      Kasieczka, Gregor and Korcari, William and Korol, Anatolii
                      and Krüger, Katja and McKeown, Peter and Rustige, Lennart},
      title        = {{C}alo{C}louds: {F}ast {G}eometry-{I}ndependent
                      {H}ighly-{G}ranular {C}alorimeter {S}imulation},
      reportid     = {PUBDB-2023-07881},
      year         = {2023},
      abstract     = {Simulating showers of particles in highly-granular
                      detectors is a key frontier in the application of machine
                      learning to particle physics.Achieving high accuracy and
                      speed with generative machine learning models would enable
                      them to augment traditional simulations and alleviate a
                      major computing constraint.This work achieves a major
                      breakthrough in this task by for the first time directly
                      generating a point-cloud of O(1000) space points with energy
                      depositions in the detector in 3D-space without relying on a
                      fixed-grid structure. This is made possible by two key
                      innovations: i) using recent improvements in generative
                      modelling we apply a diffusion model and ii) an initial even
                      higher-resolution point-cloud of up to 40000 so-called
                      GEANT4 steps which are subsequently down-sampled to the
                      desired number of up to 6000 space points.We showcase the
                      performance of this approach using the specific example of
                      simulating photon showers in the planned electromagnetic
                      calorimeter of the International Large Detector (ILD) and
                      achieve overall good modelling of physically relevant
                      distributions.},
      month         = {May},
      date          = {2023-05-30},
      organization  = {CaloChallenge Workshop , Villa
                       Mondragone (Italy), 30 May 2023 - 31
                       May 2023},
      cin          = {FTX},
      cid          = {I:(DE-H253)FTX-20210408},
      pnm          = {623 - Data Management and Analysis (POF4-623) / AIDAinnova
                      - Advancement and Innovation for Detectors at Accelerators
                      (101004761) / 05D23GU4 - Verbundprojekt 05D2022 - KISS:
                      Künstliche Intelligenz zur schnellen Simulation von
                      wissenschaftlichen Daten. Teilprojekt 1. (BMBF-05D23GU4)},
      pid          = {G:(DE-HGF)POF4-623 / G:(EU-Grant)101004761 /
                      G:(DE-Ds200)BMBF-05D23GU4},
      experiment   = {EXP:(DE-MLZ)NOSPEC-20140101},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://bib-pubdb1.desy.de/record/600313},
}