1. Fast muon simulation in the JUNO experiment with neural networks.
- Author
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Fang, Wenxing, Li, Weidong, and Lin, Tao
- Subjects
- *
ARTIFICIAL neural networks , *MUONS , *PHYSICS experiments , *NEUTRINO mass , *PARTICLE detectors - Abstract
The Jiangmen Underground Neutrino Observatory (JUNO) experiment is set to begin data taking in 2024 with the aim of determining the neutrino mass ordering (NMO) to a significance of 3 σ after 6 years of data taking. Achieving this goal requires effective background suppression, with the background induced by cosmic-ray muons being one of the most significant sources of interference in the NMO study. Accurately simulating the cosmic-ray muon background is crucial for the success of the experiment, but the sheer number of optical photons produced by the muon makes this detector simulation process extremely time-consuming using traditional methods such as Geant4. This paper presents a fast muon simulation method that employs neural networks to expedite the simulation process. Our approach achieves an order-of-magnitude speed-up in simulation time compared to Geant4, while still producing accurate results. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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