November 11, 2022 Master Thesis Open Access

Wemmer, Florian

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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">Belle II</subfield>
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    <subfield code="a">Calorimeter</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="a">&lt;p&gt;This thesis presents the implementation and performance of the GravNet algorithm for the photon&lt;br&gt;
energy reconstruction in the Belle II electromagnetic calorimeter. GravNet is a machine learning&lt;br&gt;
algorithm based on the concept of graph neural networks. The Belle II Analysis Software Frame-&lt;br&gt;
work is the currently used reconstruction framework that serves as the baseline for comparison in&lt;br&gt;
several studies. GravNet solves many of the conceptual restrictions that limit the performance&lt;br&gt;
of the traditional reconstruction approach, especially in the presence of high levels of beam&lt;br&gt;
background. The studies in this thesis are considered a first validation and are exclusively based&lt;br&gt;
on Monte Carlo generated and simulated data. The GravNet implementation outperforms the&lt;br&gt;
baseline energy resolutions over a large range of photon energies from 0.01GeV to 3.0 GeV by&lt;br&gt;
up to 20 %. In addition, the studies demonstrate substantial improvements of up to 15 % in the&lt;br&gt;
reconstruction of neutral pions from the invariant mass of two-photon systems. GravNet proves&lt;br&gt;
to be a viable and versatile reconstruction algorithm with a promising outlook for a broad range&lt;br&gt;
of present and future applications.&lt;/p&gt;</subfield>
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    <subfield code="a">Wemmer, Florian</subfield>
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