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@PHDTHESIS{Engelke:642304,
      author       = {Engelke, Frederic},
      othercontributors = {Borras, Kerstin and Krämer, Michael},
      title        = {{S}earches for supersymmetric dark matter in semileptonic
                      final states at the {CMS} experiment employing angular
                      correlation and deep learning techniques followed by a
                      reinterpretation in the p{MSSM}19 framework},
      school       = {RWTH Aachen},
      type         = {Dissertation},
      reportid     = {PUBDB-2025-05474},
      pages        = {167},
      year         = {2025},
      note         = {Dissertation, RWTH Aachen, 2025},
      abstract     = {The nature of dark matter (DM) remains one of the most
                      compelling mysteries in modernphysics. Despite DM exceeding
                      the visible (baryonic) matter by a factor of four, itsorigin
                      and properties are yet to be understood. This thesis
                      explores the dark matterproblem through the framework of
                      supersymmetry (SUSY), a theoretical extension ofthe Standard
                      Model of particle physics. Using data collected during the
                      Large HadronCollider (LHC) Run 2 (2016-2018) by the Compact
                      Muon Solenoid (CMS) experiment,with an integrated luminosity
                      of L = 138 fb−1, multiple analysis strategies are
                      employedto search for signatures of SUSY particles that
                      present viable DM candidates.The first analysis utilizes a
                      cut-and-count approach targeting the m˜g-m˜χ01 mass
                      planevia angular correlation between selected physics
                      objects, combined with a data-drivenmethod to address
                      limitations in background modeling via transfer factors and
                      correc-tions. This analysis achieved exclusion limits for
                      gluino masses up to 2050 GeV andneutralino masses up to 1070
                      GeV. Subsequently, these results were reinterpreted
                      withinthe phenomenological MSSM framework (pMSSM19),
                      constraining additional SUSY pa-rameters and highlighting
                      potential regions of interest based on observed data
                      excesses.To further enhance the sensitivity, a machine
                      learning-based approach was developed,utilizing a deep
                      neural network (DNN) to classify collision events and define
                      signal regionsbased on DNN scores. This novel methodology
                      expands the exclusion limits up to 1450GeV for m˜χ01 and
                      up to 2230 GeV for m˜g and demonstrates the advantages of
                      sophisticatedcomputational techniques in modern collider
                      analyses.Also, the HO, the outer hadron calorimeter of the
                      CMS detector, was evaluated as apotential trigger system for
                      long-lived particle (LLP) detection, addressing challenges
                      inidentifying signatures predicted by SUSY theories.},
      cin          = {CMS},
      cid          = {I:(DE-H253)CMS-20120731},
      pnm          = {611 - Fundamental Particles and Forces (POF4-611) / GRK
                      2497 - GRK 2497: Physik der schwersten Teilchen am Large
                      Hadron Collider (400140256)},
      pid          = {G:(DE-HGF)POF4-611 / G:(GEPRIS)400140256},
      experiment   = {EXP:(DE-H253)LHC-Exp-CMS-20150101},
      typ          = {PUB:(DE-HGF)11},
      doi          = {10.3204/PUBDB-2025-05474},
      url          = {https://bib-pubdb1.desy.de/record/642304},
}