November 11, 2022 Master Thesis Open Access

Wemmer, Florian

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{
  "abstract": "<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>", 
  "author": [
    {
      "family": "Wemmer, Florian"
    }
  ], 
  "id": "22142", 
  "issued": {
    "date-parts": [
      [
        2022, 
        11, 
        11
      ]
    ]
  }, 
  "language": "eng", 
  "title": "Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks", 
  "type": "thesis"
}