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@PHDTHESIS{Stein:991721,
      author       = {Stein, Annika},
      othercontributors = {Schmidt, Alexander and Krämer, Michael},
      title        = {{N}ovel jet flavour tagging algorithms exploiting
                      adversarial deep learning techniques with efficient
                      computing methods and preparation of open data for
                      robustness studies},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {RWTH Aachen University},
      reportid     = {RWTH-2024-07840},
      pages        = {1 Online-Ressource : Illustrationen},
      year         = {2024},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University; Dissertation, RWTH Aachen University, 2024},
      abstract     = {Machine learning algorithms are an indispensable tool for
                      science. Precision tests of the standard model of particle
                      physics and searches for processes involving elementary
                      particles are facilitated with novel reconstruction
                      algorithms that exploit complex neural network
                      architectures. Such applications however oftentimes rely on
                      simulated processes, one example being the identification of
                      the flavour of quarks or gluons initiating particle jets
                      (jet flavour tagging). Besides other experimental sources of
                      uncertainties, the efficiency uncertainties stemming from
                      object identification involving neural networks can
                      contribute significantly to final results, expressed for
                      example as uncertainties in a signal strength. Tests with
                      control regions reveal differences in performance between
                      samples obtained through simulation and those from detector
                      data, meaning that calibration is required. With this in
                      mind, this thesis aims at providing not only efficient
                      measures to mitigate this performance gap between data and
                      simulation from the ground up (especially when the algorithm
                      performs very well on simulation), but also derives concepts
                      that assist in understanding why the proposed approaches
                      work. Building up from early versions of adversarial attacks
                      and defenses, a new algorithm, denoted Normed Gradient
                      Method (NGM), is introduced and adapted for physics
                      applications for the first time. This also marks the
                      introduction of a state-of-the-art transformer architecture
                      for small-radius jets for the CMS experiment. In combination
                      with NGM, the currently best performance metrics for this
                      task at CMS are achieved, improving over previous
                      algorithms. The network maintains high performance even
                      under exposition to systematic modifications of inputs. It
                      is thus the first time an (adversarially) robust algorithm
                      is introduced for the official reconstruction software of a
                      high-energy particle detector. Event throughput improves
                      compared to an algorithm that achieves slightly worse
                      performance. The efficient integration was significantly
                      facilitated by a novel software framework specifically
                      developed for jet flavour tagging that is capable of
                      performance studies with data, although the neural network
                      is still in development (training) stage. The
                      time-to-insight from neural network training to reliable
                      performance studies is significantly reduced. This is
                      possible, because the framework is built around only one
                      data tier that serves multiple purposes to study the neural
                      network. Besides the work within the context of one
                      experiment, another focus is the preparation and utilization
                      of CERN Open Data for robustness studies. This last part of
                      this thesis is dedicated to the conversion of already
                      available open datasets, which can only be used with
                      experiment-specific software, into machine learning-friendly
                      formats. The result is the first open dataset that allows
                      small-radius jet flavour tagging studies with simulation and
                      recorded detector data for a broader audience of data
                      scientists that do not necessarily know how to operate the
                      experiment software. As every experiment provides unique
                      data, tools, and problem statements, the concept of a
                      wrapper structure is introduced, which allows applying a
                      core set of adversarial techniques in a toolbox to the
                      different use cases.},
      cin          = {133920 ; 133910 / 130000},
      ddc          = {530},
      cid          = {$I:(DE-82)133920_20180228$ / $I:(DE-82)130000_20140620$},
      typ          = {PUB:(DE-HGF)11},
      doi          = {10.18154/RWTH-2024-07840},
      url          = {https://publications.rwth-aachen.de/record/991721},
}