06 October 2023
Science Building
America/Chicago timezone
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The effect of Neural Networks on hadron calorimetry with Cherenkov fiber calorimeters
Improving the reconstruction of hadronic shower with similar precision as electromagnetic particles in high-energy physics calorimeters has been a longstanding quest. The dual-readout method (DREAM) measuring the scintillation and Cherenkov light simultaneously has shown the significant improvement of the energy reconstruction for hadronic showers over the traditional simple signal sum method. We simulated a finely-segmented Cherenkov fiber calorimeter using GEANT4 and used neural network to reconstruct the energy. We compare its performance to the dual-readout method and discuss how the neural networks (graph neural networks) improve the energy reconstruction of hadronic shower.
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Id: 33
Place: Science Building
Texas Tech University, Physics & Astronomy 

Room: 106
Starting date:
06-Oct-2023   13:00 (America/Chicago)
Duration: 03h00'
Contribution type: Poster
Primary Authors: Mr. MARGETA-CACACE, Harold (Texas Tech University)
Co-Authors: LAMICHHANE, Kamal (Texas Tech University)
Prof. AKCHURIN, Nural (TTU)
KUNORI, Shuichi (TTU)
Presenters: Mr. MARGETA-CACACE, Harold