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@ARTICLE{Buss:631263,
      author       = {Buss, Thorsten and Gaede, Frank and Kasieczka, Gregor and
                      Korol, Anatolii and Krüger, Katja and McKeown, Peter and
                      Mozzanica, Martina},
      title        = {{C}alo{H}adronic : a diffusion model for the generation of
                      hadronic showers},
      reportid     = {PUBDB-2025-01935, DESY-25-094. arXiv:2506.21720},
      year         = {2025},
      abstract     = {Simulating showers of particles in highly-granular
                      calorimeters is a key frontier in the application of machine
                      learning to particle physics. Achieving high accuracy and
                      speed with generative machine learning models can enable
                      them to augment traditional simulations and alleviate a
                      major computing constraint. Recent developments have shown
                      how diffusion based generative shower simulation approaches
                      that do not rely on a fixed structure, but instead generate
                      geometry-independent point clouds, are very efficient. We
                      present a transformer-based extension to previous
                      architectures which were developed for simulating
                      electromagnetic showers in the highly granular
                      electromagnetic calorimeter of the International Large
                      Detector, ILD. The attention mechanism now allows us to
                      generate complex hadronic showers with more pronounced
                      substructure across both the electromagnetic and hadronic
                      calorimeters. This is the first time that machine learning
                      methods are used to holistically generate showers across the
                      electromagnetic and hadronic calorimeter in highly granular
                      imaging calorimeter systems. The code is available at
                      https://github.com/FLC-QU-hep/CaloHadronic.},
      keywords     = {Instrumentation and Detectors (physics.ins-det) (Other) /
                      Machine Learning (cs.LG) (Other) / High Energy Physics -
                      Experiment (hep-ex) (Other) / High Energy Physics -
                      Phenomenology (hep-ph) (Other) / Data Analysis, Statistics
                      and Probability (physics.data-an) (Other) / FOS: Physical
                      sciences (Other) / FOS: Computer and information sciences
                      (Other)},
      cin          = {FTX},
      cid          = {I:(DE-H253)FTX-20210408},
      pnm          = {611 - Fundamental Particles and Forces (POF4-611) / DFG
                      project G:(GEPRIS)390833306 - EXC 2121: Quantum Universe
                      (390833306) / 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)POF4-611 / G:(GEPRIS)390833306 /
                      G:(EU-Grant)101004761 / G:(DE-Ds200)BMBF-05D23GU4},
      experiment   = {EXP:(DE-MLZ)NOSPEC-20140101},
      typ          = {PUB:(DE-HGF)25},
      eprint       = {2506.21720},
      howpublished = {arXiv:2506.21720},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2506.21720;\%\%$},
      doi          = {10.3204/PUBDB-2025-01935},
      url          = {https://bib-pubdb1.desy.de/record/631263},
}