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@PHDTHESIS{McKeown:607309,
      author       = {McKeown, Peter},
      othercontributors = {Gaede, Frank and Kasieczka, Gregor},
      title        = {{D}evelopment and {P}erformance of a {F}ast {S}imulation
                      {T}ool for {S}howers in {H}igh {G}ranularity {C}alorimeters
                      based on {D}eep {G}enerative {M}odels},
      school       = {Universität Hamburg},
      type         = {Dissertation},
      address      = {Hamburg},
      publisher    = {Verlag Deutsches Elektronen-Synchrotron DESY},
      reportid     = {PUBDB-2024-01825, DESY-THESIS-2024-008},
      series       = {DESY-THESIS},
      pages        = {186},
      year         = {2024},
      note         = {Dissertation, Universität Hamburg, 2024},
      abstract     = {Modern high energy physics experiments fundamentally rely
                      on large quantities of simulateddata, placing significant
                      demands on the available computational resources. Machine
                      learningmethods based on deep generative models promise to
                      reduce the compute time required tosimulate particle showers
                      in the calorimeter system, which constitutes the most
                      computationallyintensive part of a full detector
                      simulation.This work focuses on the development of a first
                      simulation tool based on deep generativemodels for shower
                      simulation in highly granular calorimeters, and subsequently
                      studies itsperformance in a realistic detector geometry. In
                      order to apply these models in a generalsimulation, they
                      must provide a suitable detector response for particles
                      incident under variousangles to, and at various positions
                      in, the detector. Crucially, the physics performance
                      afterreconstruction must remain high, which is the ultimate
                      target of such a simulator.We initially extend the
                      performant Bounded Information Bottleneck Autoencoder
                      (BIB-AE) to simulate showers from photons with varying
                      incident energy and angle to the surface ofthe
                      electromagnetic calorimeter of the International Large
                      Detector (ILD), before studying thesingle particle
                      performance of the model in terms of key calorimetric
                      observables, both beforeand after reconstruction. We then
                      further extend the model to handle an additional angle
                      ofincidence, as well as taking steps to deal with geometry
                      irregularities in order to allow the useof the model at
                      different positions in the calorimeter.As a next step, we
                      describe a generic library that enables the use of
                      generative models withGeant4 and DD4hep, allowing a full
                      integration into standard software ecosystems used inhigh
                      energy physics. We outline the integration of the BIB-AE
                      into this library, allowing afair benchmark of the
                      computational performance of the model. We then simulate
                      showers atdifferent positions with the model, in order to
                      investigate the effects of performing simulationsin an
                      irregular calorimeter geometry.Finally, we study the
                      performance of the BIB-AE when used to simulate photons
                      fromneutral pion decays in the process
                      $e^{+}e^{-}\rightarrow \tau^{+}\tau^{-}$ in terms of key
                      physics observables. We findthat while some deviations from
                      Geant4 occur, they are typically comparable to the
                      MonteCarlo uncertainty, estimated from the performance
                      differences between Geant4 versions.},
      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) / DFG project G:(GEPRIS)390833306 - EXC 2121:
                      Quantum Universe (390833306) / 05D23GU4 - Verbundprojekt
                      05D2022 - KISS: Künstliche Intelligenz zur schnellen
                      Simulation von wissenschaftlichen Daten. Teilprojekt 1.
                      (BMBF-05D23GU4) / PHGS, VH-GS-500 - PIER Helmholtz Graduate
                      School $(2015_IFV-VH-GS-500)$},
      pid          = {G:(DE-HGF)POF4-623 / G:(EU-Grant)101004761 /
                      G:(GEPRIS)390833306 / G:(DE-Ds200)BMBF-05D23GU4 /
                      $G:(DE-HGF)2015_IFV-VH-GS-500$},
      experiment   = {EXP:(DE-MLZ)NOSPEC-20140101},
      typ          = {PUB:(DE-HGF)3 / PUB:(DE-HGF)11},
      doi          = {10.3204/PUBDB-2024-01825},
      url          = {https://bib-pubdb1.desy.de/record/607309},
}