发表机构
University of Chinese Academy of Sciences; Institute of High Energy Physics, Chinese Academy of Sciences; North China Electric Power University（中国科学院大学; 中国科学院高能物理研究所; 华北电力大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究表征大型液体闪烁体探测器中大气中微子中性流单信号事例的模型依赖性，给出事例率与能谱，凸显江门地下中微子天文台等装置的物理潜力。
AI 中文摘要
大型液体闪烁体探测器中大气中微子的中性流相互作用为研究单信号事例（以下简称单事例）提供了新机遇，这类事例的特征是沉积的瞬时能量处于MeV至GeV量级，且无可识别的延迟信号。本工作系统研究了大气中微子单事例的模型依赖性，该依赖性源于初级中微子-原子核相互作用、剩余原子核退激以及闪烁体中的次级相互作用。研究结果表明，主要的模型依赖性源自初级中微子-原子核相互作用，尤其是碳核上的中性流过程；退激对单事例筛选至关重要，但在实际模型间仅导致相对较小的能谱变化；次级相互作用效应总体上也属于次要因素。我们还给出了中性流单事例的预测事例率和瞬时能谱，以及带电流贡献；此外，我们估算了亚100 MeV大气中微子与自由质子弹性散射的低能贡献。这些结果凸显了当前及未来大型液体闪烁体探测器（如江门地下中微子天文台）的物理潜力，可用于研究大气中微子单事例、探测中微子-原子核相互作用模型，并改进稀有事例搜寻的本底估计。
英文摘要
Neutral-current interactions of atmospheric neutrinos in large liquid scintillator detectors offer a new opportunity to study single-signal events (hereafter singles), characterized by a prompt energy deposition on the MeV-to-GeV scale and no identified delayed signal. In this work, we systematically investigate the model dependence of atmospheric-neutrino singles due to the primary neutrino-nucleus interaction, residual-nucleus de-excitation, and secondary interactions in the scintillator. Our results show that the dominant model dependence originates from the primary neutrino-nucleus interaction, especially for neutral-current processes on carbon, whereas de-excitation is essential for the singles selection yet leads to relatively small spectral variations among realistic models. Secondary-interaction effects are also subdominant overall. We further present the predicted event rates and prompt-energy spectra for neutral-current singles, along with the charged-current contribution. Separately, we estimate the low-energy contribution from elastic scattering of sub-\SI{100}{\MeV} atmospheric neutrinos on free protons. These results highlight the physics potential of current and future large liquid scintillator detectors, such as the Jiangmen Underground Neutrino Observatory, to study atmospheric-neutrino singles, probe neutrino-nucleus interaction models, and improve background estimates for rare-event searches.
