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@PHDTHESIS{Quadfasel:626057,
      author       = {Quadfasel, Tobias},
      othercontributors = {Kasieczka, Gregor and Schleper, Peter},
      title        = {{A} {M}odel-{A}gnostic {S}earch for {B}eyond the {S}tandard
                      {M}odel {P}hysics in the {D}ijet {T}opology using {M}achine
                      {L}earning},
      school       = {University of Hamburg},
      type         = {Dissertation},
      reportid     = {PUBDB-2025-01279},
      pages        = {229},
      year         = {2024},
      note         = {Dissertation, University of Hamburg, 2024},
      abstract     = {Despite significant efforts to search for new beyond the
                      Standard Model phenomena at the LHC physics program and
                      beyond, no evidence has been found so far. Model-agnostic
                      searches complement current search efforts, allowing for the
                      detection of potential new physics anomalies without
                      targeting a particular signal model.This thesis discusses
                      the development and application of novel data-driven methods
                      for model-agnostic anomaly detection. In particular, a new
                      method based on neural density estimation and weak
                      classification – named Cathode – is developed, achieving
                      state-of-the-art performance on a commonly used benchmark
                      data set. The first application of Cathode and other Machine
                      Learning-based methods on proton-proton collision data taken
                      at the CMS experiment from 2016 to 2018 at a centre-of-mass
                      energy of √𝑠 = 13 TeV is discussed. Specifically,
                      hadronic resonances in the two-jet final state are targeted.
                      A generic search for new physics in the invariant mass
                      spectrum of the two jets revealed no significant excess. In
                      terms of cross-section limits, Cathode achieves the most
                      sensitive results for several of the tested mass hypotheses
                      of an 𝑋 → 𝑌 𝑌 ′ → 4𝑞 signal model, with an
                      optimal improvement over an inclusive search of factor
                      1.9.Several improvements to the original Cathode algorithm
                      are proposed. Latent Cathode allows for signal extraction in
                      the case of strong correlations between input features and
                      mass by conducting the weak classification in latent space.
                      Using Gradient Boosting classifiers instead of Deep
                      Learning-based methods in weak classification is shown to be
                      considerably more robust against uninformative features,
                      allowing for model-agnostic searches without prior feature
                      selection.},
      pnm          = {PHGS, VH-GS-500 - PIER Helmholtz Graduate School
                      $(2015_IFV-VH-GS-500)$},
      pid          = {$G:(DE-HGF)2015_IFV-VH-GS-500$},
      typ          = {PUB:(DE-HGF)11},
      urn          = {urn:nbn:de:gbv:18-ediss-118211},
      url          = {https://bib-pubdb1.desy.de/record/626057},
}