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Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks

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arxiv 2306.04179 v2 pith:BQD43OZO submitted 2023-06-07 hep-ex physics.ins-det

Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks

F. Wemmer , I. Haide , J. Eppelt , T. Ferber , A. Beaubien , P. Branchini , M. Campajola , C. Cecchi
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P. Cheema G. De Nardo C. Hearty A. Kuzmin S. Longo E. Manoni F. Meier M. Merola K. Miyabayashi S. Moneta M. Remnev J. M. Roney J.-G. Shiu B. Shwartz Y. Unno R. van Tonder R. Volpe
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classification hep-ex physics.ins-det
keywords reconstructionbellephotonsalgorithmbackgroundsbeamcalorimeterenergy
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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 E < 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.

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  1. Real-time graph neural networks on FPGAs for the Belle II electromagnetic calorimeter

    physics.ins-det 2026-02 conditional novelty 6.0

    A GNN-based calorimeter clustering and signal classifier ran on an FPGA inside the Belle II L1 trigger readout path, improving position resolution and photon separation at the cost of exceeding the trigger decision latency.