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@ARTICLE{Buhmann:611512,
      author       = {Buhmann, Erik and Gaede, Frank and Kasieczka, Gregor and
                      Korol, Anatolii and Korcari, William and Krüger, Katja and
                      McKeown, Peter},
      title        = {{C}alo{C}louds {II}: ultra-fast geometry-independent
                      highly-granular calorimeter simulation},
      journal      = {Journal of Instrumentation},
      volume       = {19},
      number       = {04},
      issn         = {1748-0221},
      address      = {London},
      publisher    = {Inst. of Physics},
      reportid     = {PUBDB-2024-04949, arXiv:2309.05704. DESY-23-130.
                      DESY-23-130. arXiv:2309.05704},
      pages        = {P04020},
      year         = {2024},
      note         = {30 pages, 7 figures, 3 tables, code available at
                      https://github.com/FLC-QU-hep/CaloClouds-2},
      abstract     = {Fast simulation of the energy depositions in high-granular
                      detectors is needed for future collider experiments with
                      ever increasing luminosities. Generative machine learning
                      (ML) models have been shown to speed up and augment the
                      traditional simulation chain in physics analysis. However,
                      the majority of previous efforts were limited to models
                      relying on fixed, regular detector readout geometries. A
                      major advancement is the recently introduced CaloClouds
                      model, a geometry-independent diffusion model, which
                      generates calorimeter showers as point clouds for the
                      electromagnetic calorimeter of the envisioned International
                      Large Detector (ILD). In this work, we introduce CaloClouds
                      II which features a number of key improvements. This
                      includes continuous time score-based modelling, which allows
                      for a 25 step sampling with comparable fidelity to
                      CaloClouds while yielding a $6\times$ speed-up over Geant4
                      on a single CPU ($5\times$ over CaloClouds). We further
                      distill the diffusion model into a consistency model
                      allowing for accurate sampling in a single step and
                      resulting in a $46\times$ ($37\times$) speed-up. This
                      constitutes the first application of consistency
                      distillation for the generation of calorimeter showers.},
      keywords     = {diffusion: model (INSPIRE) / calorimeter: electromagnetic
                      (INSPIRE) / showers (INSPIRE) / machine learning (INSPIRE) /
                      ILD detector (INSPIRE) / numerical calculations (INSPIRE) /
                      programming (INSPIRE) / numerical methods (INSPIRE) /
                      Simulation methods and programs (autogen) / Analysis and
                      statistical methods (autogen) / Calorimeter methods
                      (autogen)},
      cin          = {FTX},
      ddc          = {610},
      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) /
                      DFG project G:(GEPRIS)390833306 - EXC 2121: Quantum Universe
                      (390833306)},
      pid          = {G:(DE-HGF)POF4-623 / G:(EU-Grant)101004761 /
                      G:(DE-Ds200)BMBF-05D23GU4 / G:(GEPRIS)390833306},
      experiment   = {EXP:(DE-MLZ)NOSPEC-20140101},
      typ          = {PUB:(DE-HGF)16},
      eprint       = {2309.05704},
      howpublished = {arXiv:2309.05704},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2309.05704;\%\%$},
      UT           = {WOS:001223248900001},
      doi          = {10.1088/1748-0221/19/04/P04020},
      url          = {https://bib-pubdb1.desy.de/record/611512},
}