November 11, 2022 Master Thesis Open Access

Wemmer, Florian

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  "@type": "ScholarlyArticle", 
  "contributor": [], 
  "creator": [
    {
      "@type": "Person", 
      "affiliation": "KIT/ETP", 
      "name": "Wemmer, Florian"
    }
  ], 
  "datePublished": "2022-11-11", 
  "description": "<p>This thesis presents the implementation and performance of the GravNet algorithm for the photon<br>\nenergy reconstruction in the Belle II electromagnetic calorimeter. GravNet is a machine learning<br>\nalgorithm based on the concept of graph neural networks. The Belle II Analysis Software Frame-<br>\nwork is the currently used reconstruction framework that serves as the baseline for comparison in<br>\nseveral studies. GravNet solves many of the conceptual restrictions that limit the performance<br>\nof the traditional reconstruction approach, especially in the presence of high levels of beam<br>\nbackground. The studies in this thesis are considered a first validation and are exclusively based<br>\non Monte Carlo generated and simulated data. The GravNet implementation outperforms the<br>\nbaseline energy resolutions over a large range of photon energies from 0.01GeV to 3.0 GeV by<br>\nup to 20 %. In addition, the studies demonstrate substantial improvements of up to 15 % in the<br>\nreconstruction of neutral pions from the invariant mass of two-photon systems. GravNet proves<br>\nto be a viable and versatile reconstruction algorithm with a promising outlook for a broad range<br>\nof present and future applications.</p>", 
  "headline": "Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks", 
  "image": "https://publish.etp.kit.edu/static/img/logos/zenodo-gradient-round.svg", 
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    "alternateName": "eng", 
    "name": "English"
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  "keywords": [
    "Belle II", 
    "Calorimeter", 
    "Graph Neural Networks", 
    "Deep Learning", 
    "Clustering", 
    "Photon Reconstruction"
  ], 
  "name": "Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks", 
  "url": "https://publish.etp.kit.edu/record/22142"
}