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@INPROCEEDINGS{Scham:481363,
      author       = {Scham, Moritz and Bhattacharya, Soham and Borras, Kerstin
                      and Bein, Sam and Eren, Engin and Gaede, Frank and
                      Kasieczka, Gregor and Korcari, William and Krücker, Dirk
                      and McKeown, Peter},
      collaboration = {{CMS Collaboration}},
      title        = {{G}enerative modeling with {G}raph {N}eural {N}etworks for
                      the {CMS} {HGC}al},
      reportid     = {PUBDB-2022-04336},
      year         = {2022},
      abstract     = {In high energy physics, detailed and time-consuming
                      simulations are used for particle interactions with
                      detectors. For the upcoming High-Luminosity phase of the
                      Large Hadron Collider (HL-LHC), the computational costs of
                      conventional simulation tools exceeds the projected
                      computational resources. Generative machine learning is
                      expected to provide a fast and accurate alternative. The CMS
                      experiment at the LHC will use a new High Granularity
                      Calorimeter (HGCal) to cope with the high particle density.
                      The new HGCal is an imaging calorimeter with a complex
                      geometry and more than 3 million cells. We report on the
                      development of a GraphGAN to simulate particle showers under
                      these challenging conditions.},
      month         = {Apr},
      date          = {2022-04-26},
      organization  = {Center for Data and Computing in
                       Natural Sciences (CDCS) Symposium,
                       Hamburg (Germany), 26 Apr 2022 - 28 Apr
                       2022},
      cin          = {CMS},
      cid          = {I:(DE-H253)CMS-20120731},
      pnm          = {611 - Fundamental Particles and Forces (POF4-611)},
      pid          = {G:(DE-HGF)POF4-611},
      experiment   = {EXP:(DE-H253)LHC-Exp-CMS-20150101},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://bib-pubdb1.desy.de/record/481363},
}