| Autor: |
Fang Wenxing, Li Weidong, Lin Tao |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
EPJ Web of Conferences, Vol 295, p 09019 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2100-014X |
| DOI: |
10.1051/epjconf/202429509019 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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