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

作者：Jiaxi Liu, Fanrui Zeng, Hongyue Duyang, Wanlei Guo, Xinhai He, Teng Li, Zhen Liu, W. Luo, W. Y., Xiaohan Tan, Liangjian Wen, Zekun Yang, Y. P. Zhang · 发表于：Physical review. D/Physical review. D. · 年份：2025 · DOI：10.1103/fznt-z257 · 被引用次数：2 · 研究领域：Neutrino Physics Research、Astrophysics and Cosmic Phenomena、Dark Matter and Cosmic Phenomena

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 photomultiplier tube waveforms that reflect event topologies and uses them as input to machine learning models. In addition, neutron-capture information is utilized to achieve neutrino vs 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.