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@ARTICLE{OmanaKuttan:363801,
      author       = {Omana Kuttan, Manjunath and Zhou, Kai and Steinheimer, Jan
                      and Stöcker, Horst},
      title        = {{U}ltrafast, event-by-event heavy-ion simulations for
                      next-generation experiments},
      journal      = {Physical review / C},
      volume       = {112},
      number       = {5},
      issn         = {2469-9985},
      address      = {Woodbury, NY},
      publisher    = {Inst.},
      reportid     = {GSI-2026-00011},
      pages        = {054907},
      year         = {2025},
      note         = {Published by the American Physical Society under the terms
                      of the Creative Commons Attribution 4.0 International
                      license. Funded by SCOAP3.},
      abstract     = {We present a novel deep generative framework that uses
                      probabilistic diffusion models for ultrafast, event-by-event
                      simulations of heavy-ion collision output. This new
                      framework is trained on ultrarelativistic quantum molecular
                      dynamics (UrQMD) cascade data to generate a full collision
                      event output containing 26 distinct hadron species. The
                      output is represented as a point cloud, where each point is
                      defined by a particle's momentum vector and its
                      corresponding species information. Our architecture
                      integrates a normalizing flow-based condition generator that
                      encodes global event features into a latent vector, and a
                      diffusion model that synthesizes a point cloud of particles
                      based on this condition. A detailed description of the model
                      and an in-depth analysis of its performance is provided. The
                      conditional point-cloud diffusion model learns to generate
                      realistic output particles of collision events which
                      successfully reproduce the UrQMD distributions for
                      multiplicity, momentum, and rapidity of each hadron type.
                      The flexible point-cloud representation of the event output
                      preserves full event-level granularity, enabling direct
                      application to inverse problems and parameter estimation
                      tasks while also making it easily adaptable for accelerating
                      any event-by-event model calculation or detector
                      simulation.},
      cin          = {TES},
      ddc          = {530},
      cid          = {I:(DE-Ds200)TES-20160901OR397},
      pnm          = {612 - Cosmic Matter in the Laboratory (POF4-612) / FIAS -
                      Frankfurt Institute for Advanced Studies (FIAS)},
      pid          = {G:(DE-HGF)POF4-612 / G:(DE-Ds200)FIAS},
      experiment   = {$EXP:(DE-Ds200)no_experiment-20200803$},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001630418000005},
      doi          = {10.1103/wyq5-hlp5},
      url          = {https://repository.gsi.de/record/363801},
}