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@MASTERSTHESIS{ParraAsensio:617179,
      author       = {Parra Asensio, David},
      othercontributors = {Kogler, Roman and Haller, Johannes},
      title        = {{T}he {I}nfluence of {P}arton {S}hower and {H}adronization
                      {M}odels on {M}achine {L}earning{J}et {T}aggers},
      school       = {University of Hamburg},
      type         = {Bachelorarbeit},
      reportid     = {PUBDB-2024-06640},
      pages        = {54},
      year         = {2024},
      note         = {Bachelorarbeit, University of Hamburg, 2024},
      abstract     = {In simulating high-energy particle collisions, Monte Carlo
                      event generatorsemploy different models to create parton
                      showers and replicate hadronization.The choice of model can
                      influence the final state of the simulation
                      and,consequently, the subsequent analysis of the generated
                      event. This study utilizeda machine learning algorithm to
                      discern the differences between quark and gluonjets
                      generated through different approaches to showering and
                      hadronization.Initially, particle jets from dijet events
                      generated using Pythia, Sherpa and Herwigwere compared
                      across various jet- and particle-level properties. Both
                      Pythia andSherpa utilize parton showers ordered by
                      transverse momentum, while Herwigemploys angular-ordered
                      showers. However, Sherpa and Herwig share similarcluster
                      hadronization models, while Pythia uses the Lund string
                      model. Thecomparison revealed that Pythia and Sherpa were
                      the most similar across mostproperties, due to a different
                      minimum transverse mometum of jets compared toHerwig. To
                      better understand the jet tagger’s decisions, the
                      characteristics ofquark and gluon jets were analyzed for
                      each generator. It was demonstrated thatgluon jets generally
                      contain more particles, and thus less momentum and energy,to
                      varying degrees, depending on the generator. A machine
                      learning algorithmwas then trained on the previously
                      generated Pythia and Sherpa samples andsubsequently tested
                      on data from all generators under consistent conditions.
                      Themodel trained on Pythia became specialized to Pythia and
                      showed reducedaccuracy when tested on Herwig and Sherpa,
                      whereas the model trained onSherpa performed almost equally
                      well across all three test datasets. While therewere
                      indications that the different hadronization models
                      influenced this outcome,the parton shower model appeared to
                      have minimal impact.},
      cin          = {CMS},
      cid          = {I:(DE-H253)CMS-20120731},
      pnm          = {611 - Fundamental Particles and Forces (POF4-611)},
      pid          = {G:(DE-HGF)POF4-611},
      experiment   = {EXP:(DE-MLZ)NOSPEC-20140101},
      typ          = {PUB:(DE-HGF)2},
      url          = {https://bib-pubdb1.desy.de/record/617179},
}