Neutrino type identification for atmospheric neutrinos in a large homogeneous liquid scintillation detector
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915341170376704 |
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| author | Liu, Jiaxi Zeng, Fanrui Duyang, Hongyue Guo, Wanlei He, Xinhai Li, Teng Liu, Zhen Luo, Wuming Ma, Wing Yan Tan, Xiaohan Wen, Liangjian Yang, Zekun Zhang, Yongpeng |
| author_facet | Liu, Jiaxi Zeng, Fanrui Duyang, Hongyue Guo, Wanlei He, Xinhai Li, Teng Liu, Zhen Luo, Wuming Ma, Wing Yan Tan, Xiaohan Wen, Liangjian Yang, Zekun Zhang, Yongpeng |
| contents | Atmospheric neutrino oscillations are important to the study of neutrino properties, including the neutrino mass ordering problem. A good capability to identify neutrinos' flavor and neutrinos against antineutrinos is crucial in such measurements. In this paper, we present a machine-learning-based approach for identifying atmospheric neutrino events in a large homogeneous liquid scintillator detector. This method identifies features of PMT waveforms that reflect event topologies and uses them as input to machine learning models. In addition, neutron-capture information is utilized to achieve neutrino versus antineutrino discrimination. Preliminary performances based on Monte Carlo simulations are presented, which demonstrate such a detector's potential in future measurements of atmospheric neutrinos such as the one planned for the JUNO experiment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_21353 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neutrino type identification for atmospheric neutrinos in a large homogeneous liquid scintillation detector Liu, Jiaxi Zeng, Fanrui Duyang, Hongyue Guo, Wanlei He, Xinhai Li, Teng Liu, Zhen Luo, Wuming Ma, Wing Yan Tan, Xiaohan Wen, Liangjian Yang, Zekun Zhang, Yongpeng High Energy Physics - Experiment Atmospheric neutrino oscillations are important to the study of neutrino properties, including the neutrino mass ordering problem. A good capability to identify neutrinos' flavor and neutrinos against antineutrinos is crucial in such measurements. In this paper, we present a machine-learning-based approach for identifying atmospheric neutrino events in a large homogeneous liquid scintillator detector. This method identifies features of PMT waveforms that reflect event topologies and uses them as input to machine learning models. In addition, neutron-capture information is utilized to achieve neutrino versus antineutrino discrimination. Preliminary performances based on Monte Carlo simulations are presented, which demonstrate such a detector's potential in future measurements of atmospheric neutrinos such as the one planned for the JUNO experiment. |
| title | Neutrino type identification for atmospheric neutrinos in a large homogeneous liquid scintillation detector |
| topic | High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2503.21353 |