大型均匀液体闪烁体探测器中大气中微子的类型识别
Neutrino type identification for atmospheric neutrinos in a large homogeneous liquid scintillation detector
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中文总结 AI 辅助
本研究针对大气中微子测量需求，提出一种基于机器学习的大型均匀液体闪烁体探测器中微子类型识别方法，结合PMT波形特征与中子俘获信息实现味及正反中微子区分，模拟结果验证了其在JUNO等未来实验中的应用潜力。
中文摘要 AI 辅助
大气中微子振荡对研究中微子性质（包括中微子质量排序问题）具有重要意义。在这类测量中，良好的中微子味识别能力以及区分中微子与反中微子的能力至关重要。本文提出了一种基于机器学习的方法，用于在大型均匀液体闪烁体探测器中识别大气中微子事例。该方法提取反映事例拓扑结构的光电倍增管（PMT）波形特征，并将其作为机器学习模型的输入。此外，该方法还利用中子俘获信息实现中微子与反中微子的区分。本文给出了基于蒙特卡罗模拟的初步性能结果，证明了这类探测器在未来大气中微子测量（如规划中的江门中微子实验（JUNO））中的潜力。
英文摘要
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.
