% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Korol:619111,
      author       = {Korol, Anatolii and Buhmann, Erik and Gaede, Frank and
                      Kasieczka, Gregor and Krüger, Katja and McKeown, Peter and
                      Buss, Thorsten Lars Henrik and Korcari, William},
      title        = {{U}ltra-{F}ast {G}eometry-{I}ndependent {H}ighly-{G}ranular
                      {C}alorimeter {S}imulation with {D}iffusion {P}oint
                      {C}louds},
      reportid     = {PUBDB-2024-07398},
      year         = {2024},
      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. Many previous efforts were
                      limited to models relying on fixed regular grid-like
                      geometries leading to artifacts when applied to highly
                      granular calorimeters with realistic cell layouts. We
                      present CaloClouds III, a novel point cloud diffusion model
                      that allows for high-speed generation of realistic
                      electromagnetic showers due to the distillation into a
                      consistency model. The model is conditioned on incident
                      energy and impact angles and implemented into a realistic
                      DD4hep based simulation model of the ILD detector concept
                      for a future Higgs factory. This is done with the
                      DDFastShowerML library which has been developed to allow for
                      easy integration of generative fast simulation models into
                      any DD4hep based detector model. With this it is possible to
                      benchmark the performance of a generative ML model using
                      fully reconstructed physics events by comparing them against
                      the same events simulated with Geant4, thereby ultimately
                      judging the fitness of the model for application in an
                      experiment’s Monte Carlo.},
      month         = {Oct},
      date          = {2024-10-19},
      organization  = {Conference on Computing in High Energy
                       and Nuclear Physics, Kraków (Poland),
                       19 Oct 2024 - 25 Oct 2024},
      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)},
      pid          = {G:(DE-HGF)POF4-623 / G:(EU-Grant)101004761},
      experiment   = {EXP:(DE-MLZ)NOSPEC-20140101},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://bib-pubdb1.desy.de/record/619111},
}