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@PHDTHESIS{Engelke:1013266,
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 University},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2025-05419},
pages = {1 Online-Ressource : Illustrationen},
year = {2025},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University; Dissertation, RWTH Aachen University, 2025},
abstract = {The nature of dark matter (DM) remains one of the most
compelling mysteries in modern physics. Despite DM exceeding
the visible (baryonic) matter by a factor of four, its
origin and properties are yet to be understood. This thesis
explores the dark matter problem through the framework of
supersymmetry (SUSY), a theoretical extension of the
Standard Model of particle physics. Using data collected
during the Large Hadron Collider (LHC) Run 2 (2016-2018) by
the Compact Muon Solenoid (CMS) experiment, with an
integrated luminosity of $L = 138 \ \text{fb}^{-1}$,
multiple analysis strategies are employed to search for
signatures of SUSY particles that present viable DM
candidates. The first analysis utilizes a cut-and-count
approach targeting the
$m_{\tilde{g}}$-$m_{\tilde{\chi}_0^1}$ mass plane via
angular correlation between selected physics objects,
combined with a data-driven method to address limitations in
background modeling via transfer factors and corrections.
This analysis achieved exclusion limits for gluino masses up
to 2050 GeV and neutralino masses up to 1070 GeV.
Subsequently, these results were reinterpreted within the
phenomenological MSSM framework (pMSSM19), constraining
additional SUSY parameters 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 regions
based on DNN scores. This novel methodology expands the
exclusion limits up to 1450 GeV for $m_{\tilde{\chi}_0^1}$
and up to 2230 GeV for $m_{\tilde{g}}$ and demonstrates the
advantages of sophisticated computational techniques in
modern collider analyses. Also, the HO, the outer hadron
calorimeter of the CMS detector, was evaluated as a
potential trigger system for long-lived particle (LLP)
detection, addressing challenges in identifying signatures
predicted by SUSY theories.},
cin = {131910 / 130000},
ddc = {530},
cid = {$I:(DE-82)131910_20160614$ / $I:(DE-82)130000_20140620$},
pnm = {GRK 2497 - GRK 2497: Physik der schwersten Teilchen am
Large Hadron Collider (400140256)},
pid = {G:(GEPRIS)400140256},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2025-05419},
url = {https://publications.rwth-aachen.de/record/1013266},
}
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