Master Thesis Open Access

Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks

Wemmer, Florian


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        "affiliation": "KIT/ETP", 
        "name": "Wemmer, Florian"
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    "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>", 
    "keywords": [
      "Belle II", 
      "Calorimeter", 
      "Graph Neural Networks", 
      "Deep Learning", 
      "Clustering", 
      "Photon Reconstruction"
    ], 
    "language": "eng", 
    "publication_date": "2022-11-11", 
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          "affiliation": "KIT/ETP", 
          "name": "Ferber, Torben"
        }, 
        {
          "affiliation": "KIT/ETP", 
          "name": "Klute, Markus"
        }
      ], 
      "university": "Karlsruhe Institute of Technology (KIT)"
    }, 
    "title": "Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks"
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    97
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