Master Thesis Open Access
Wemmer, Florian
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<subfield code="c">Karlsruhe Institute of Technology (KIT)</subfield>
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<subfield code="a"><p>This thesis presents the implementation and performance of the GravNet algorithm for the photon<br>
energy reconstruction in the Belle II electromagnetic calorimeter. GravNet is a machine learning<br>
algorithm based on the concept of graph neural networks. The Belle II Analysis Software Frame-<br>
work is the currently used reconstruction framework that serves as the baseline for comparison in<br>
several studies. GravNet solves many of the conceptual restrictions that limit the performance<br>
of the traditional reconstruction approach, especially in the presence of high levels of beam<br>
background. The studies in this thesis are considered a first validation and are exclusively based<br>
on Monte Carlo generated and simulated data. The GravNet implementation outperforms the<br>
baseline energy resolutions over a large range of photon energies from 0.01GeV to 3.0 GeV by<br>
up to 20 %. In addition, the studies demonstrate substantial improvements of up to 15 % in the<br>
reconstruction of neutral pions from the invariant mass of two-photon systems. GravNet proves<br>
to be a viable and versatile reconstruction algorithm with a promising outlook for a broad range<br>
of present and future applications.</p></subfield>
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<subfield code="a">Wemmer, Florian</subfield>
<subfield code="u">KIT/ETP</subfield>
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<subfield code="a">Belle II</subfield>
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<subfield code="a">Calorimeter</subfield>
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<subfield code="a">Graph Neural Networks</subfield>
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<subfield code="a">Deep Learning</subfield>
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<subfield code="a">Clustering</subfield>
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<subfield code="a">Photon Reconstruction</subfield>
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<subfield code="c">2022-11-11</subfield>
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<subfield code="a">eng</subfield>
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<subfield code="a">Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks</subfield>
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<subfield code="a">Ferber, Torben</subfield>
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<subfield code="a">Klute, Markus</subfield>
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