Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks

Sumitted to PubDB: 2023-01-25

Category: Master Thesis, Visibility: Public

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Authors	Torben Ferber, Florian Wemmer
Non-Belle II authors	Markus Klute
Date	Jan. 1, 2022
Belle II Number	BELLE2-MTHESIS-2023-001
Abstract	This thesis presents the implementation and performance of the GravNet algorithm for the photon energy reconstruction in the Belle II electromagnetic calorimeter. GravNet is a machine learning algorithm based on the concept of graph neural networks. The Belle II Analysis Software Framework is the currently used reconstruction framework that serves as the baseline for comparison in several studies. GravNet solves many of the conceptual restrictions that limit the performance of the traditional reconstruction approach, especially in the presence of high levels of beam background. The studies in this thesis are considered a first validation and are exclusively based on Monte Carlo generated and simulated data. The GravNet implementation outperforms the baseline energy resolutions over a large range of photon energies from 0.01 GeV to 3.0 GeV by up to 20%. In addition, the studies demonstrate substantial improvements of up to 15% in the reconstruction of neutral pions from the invariant mass of two-photon systems. GravNet proves to be a viable and versatile reconstruction algorithm with a promising outlook for a broad range of present and future applications.
Conference	Karlsruhe