Speaker
Description
Jiangmen Underground Neutrino Observatory (JUNO) experiment will start data taking in 2023. It aims to determine the neutrino mass ordering (NMO) with 3 sigma significance within 6 years. To achieve this goal, the backgrounds should be suppressed as much as possible. The cosmic-ray muon is one of the most important backgrounds for NMO study which should be taken carefully. Due to the high-energy muon that could produce millions of optic photons in the liquid scintillator in the JUNO center detector, it takes hours for Geant4 to simulate one high-energy muon which is extremely time-consuming. This contribution proposes a fast muon simulation method with neural networks. The basic idea is to use neural networks to simulate the number of optic photons detected by each photomultiplier tube (PMT) as well as the detected time of the photons. With this method, the simulation of the transportation of the optic photons can be avoided, which helps to reduce the simulation time significantly. Finally, the performance of this fast muon simulation is checked and shows promise. At least an order of magnitude speed-up can be achieved.
| Consider for long presentation | Yes |
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