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Photon Reconstruction in the BelleII Calorimeter Using Graph Neural Networks
Description
We present the study of a fuzzy clustering algorithm for the Belle II electromagnetic calorimeter using Graph Neural Networks. We use a realistic detector simulation including simulated beam backgrounds and focus on the reconstruction of both isolated and overlapping photons. We find significant improvements of the energy resolution compared to the currently used reconstruction algorithm for both isolated and overlapping photons of more than 30% for photons with energies E𝛾 < 0.5 GeV and high levels of beam backgrounds. Overall, the GNN reconstruction improves the resolution and reduces the tails of the reconstructed energy distribution and therefore is a promising option for the upcoming high luminosity running of Belle II.
Journal
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- Computing and Software for Big Science
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Computing and Software for Big Science 7 (1), 2023
Springer Nature
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Keywords
Details 詳細情報について
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- CRID
- 1050866183044003584
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- ISSN
- 25102044
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- HANDLE
- 10935/0002006050
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- Text Lang
- en
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- Article Type
- journal article
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- Data Source
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- IRDB