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arXiv · 2503.21353

Neutrino type identification for atmospheric neutrinos in a large homogeneous liquid scintillation detector

Jiaxi Liu·Fanrui Zeng·Hongyue Duyang·Wanlei Guo·Xinhai He·Teng Li·Zhen Liu·Wuming Luo·Wing Yan Ma·Xiaohan Tan·Liangjian Wen·Zekun Yang·Yongpeng Zhang
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Abstract

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.

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Jiaxi Liu, Fanrui Zeng, Hongyue Duyang, Wanlei Guo, Xinhai He, Teng Li, Zhen Liu, Wuming Luo, Wing Yan Ma, Xiaohan Tan, Liangjian Wen, Zekun Yang, Yongpeng Zhang. 2025-03-27. Neutrino type identification for atmospheric neutrinos in a large homogeneous liquid scintillation detector. https://arxiv.org/abs/2503.21353

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