Graph neural networks can identify and remove unwanted beam background depositions in the Belle II calorimeter to improve hadronic clustering and reduce fake photon clusters.
Geant4—a Simulation Toolkit
2 Pith papers cite this work. Polarity classification is still indexing.
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Neural network reconstructs cosmic-ray trajectories to better than 1.4° angular resolution and separates charges to >95% accuracy for Z≤8 using Geant4-simulated data for the RadMap Telescope.
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Using Graph Neural Networks for hadronic clustering and to reduce beam background in the Belle~II electromagnetic calorimeter
Graph neural networks can identify and remove unwanted beam background depositions in the Belle II calorimeter to improve hadronic clustering and reduce fake photon clusters.
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A Neural-Network Framework for Tracking and Identification of Cosmic-Ray Nuclei in the RadMap Telescope
Neural network reconstructs cosmic-ray trajectories to better than 1.4° angular resolution and separates charges to >95% accuracy for Z≤8 using Geant4-simulated data for the RadMap Telescope.