Master Thesis Open Access
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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"subtype": "master-thesis",
"title": "Master Thesis",
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"supervisors": [
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"affiliation": "KIT/ETP",
"name": "Ferber, Torben"
},
{
"affiliation": "KIT/ETP",
"name": "Klute, Markus"
}
],
"university": "Karlsruhe Institute of Technology (KIT)"
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"title": "Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks"
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97
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