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@MASTERSTHESIS{Sohail:622633,
      author       = {Sohail, Zain Muhammad},
      othercontributors = {Berkels, Benjamin and Rossnagel, Kai},
      title        = {{D}enoising {M}ethods for {M}ulti-{D}imensional
                      {P}hotoemission {S}pectroscopy},
      school       = {RWTH Aachen},
      type         = {Masterarbeit},
      address      = {Aachen},
      publisher    = {RWTH Aachen},
      reportid     = {PUBDB-2025-00428},
      pages        = {92},
      year         = {2024},
      note         = {ProposalID: 20010115 and 20010001 additionally;
                      Masterarbeit, RWTH Aachen, 2025},
      abstract     = {The probabilistic nature of photoemission, combined with
                      the exploration of large multidimensional parameter
                      spaces–including momentum, energy, time and spin
                      polarization–necessitates time-intensive data acquisition
                      to ensure statistical robustness. These measurements are
                      especially important for capturing ultrafast phenomena,
                      where pulsed light sources, such as free-electron lasers
                      (FELs), become indispensable due to their ability to deliver
                      high-brightness, ultrashort X-ray pulses. However, the low
                      repetition rates of current FEL sources significantly extend
                      acquisition times, impeding the real-time decision-making
                      that could otherwise enhance experimental results. Hence, to
                      optimize experimental outcomes for the valuable beamtimes,
                      techniques that can harness the structures and correlations
                      within the multidimensional space are necessary to
                      accelerate data acquisition without compromising data
                      fidelity.To address these challenges, we present an
                      investigation into advanced denoising methodologies for
                      multidimensional photoemission spectroscopy (MPES) data
                      acquired with time-of-flight momentum microscopes. We focus
                      on two key approaches: (1) employing BM3D with variance
                      stabilization through the Anscombe transform in moderately
                      noisy datasets and (2) leveraging a deep learning-based 3D
                      UNET architecture, based on the Noise2Noise paradigm,
                      excelling in low-count regimes where classical methods
                      fail.We further establish that the photoemitted electron
                      distributions measured with SASE FELs deviate from
                      traditional Poissonian statistics, instead following
                      negative binomial statistics, an outcome that has
                      implications for denoising strategies in the MPES data.Our
                      results demonstrate that BM3D delivers robust denoising
                      performance for datasets with moderate average-counts (on
                      the order of 1 $×10^−2$ counts per voxel). However, in
                      extreme low-count regimes (on the order of 1 $×10^−3$
                      counts per voxel), where most conventional denoising
                      techniques fail, the deep learning-based approach achieves
                      exceptional denoising performance. Remarkably, we show that
                      MPES datasets acquired in just 10 minutes using an FEL light
                      source can, when processed with our deep learning model,
                      reveal key features that remain indistinguishable even after
                      hours of conventional measurement. The findings presented
                      have therefore the potential to streamline data acquisition
                      at both laboratory-scale table-top setups and large-scale
                      facilities such as FEL FLASH. By optimizing acquisition
                      parameters, researchers can conserve valuable beamtime or
                      extend the scope oftheir studies to broader parameter
                      spaces, results that hold broader implications for related
                      experimental techniques.},
      cin          = {DOOR ; HAS-User},
      cid          = {I:(DE-H253)HAS-User-20120731},
      pnm          = {6G2 - FLASH (DESY) (POF4-6G2) / DFG project
                      G:(GEPRIS)434434223 - SFB 1461: Neuroelektronik: Biologisch
                      inspirierte Informationsverarbeitung (434434223) /
                      FS-Proposal: F-20180577 (F-20180577)},
      pid          = {G:(DE-HGF)POF4-6G2 / G:(GEPRIS)434434223 /
                      G:(DE-H253)F-20180577},
      experiment   = {EXP:(DE-H253)F-PG2-20150101},
      typ          = {PUB:(DE-HGF)19},
      url          = {https://bib-pubdb1.desy.de/record/622633},
}