利用似然法和图神经网络方法重建NEON中的类簇射事例
Reconstruction of Shower-like Events in NEON Using Likelihood and Graph Neural Network Methods
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中文总结 AI 辅助
本研究针对南海中微子观测站（NEON）的类簇射事例，构建了结合最大似然估计（MLE）与图神经网络（GNN）的重建框架，提升了不同能区的角分辨率与能量分辨率，为NEON及下一代深海中微子望远镜提供了基准方法。
中文摘要 AI 辅助
南海中微子观测站（NEON）是拟议部署在南海的深海中微子望远镜。类簇射事例的精确重建对中微子能量测量和多信使天文学至关重要，但由于海水光学衰减、不规则探测器几何结构以及大量40K环境本底，该任务面临重大挑战。本研究提出了首个针对NEON类簇射事例的综合重建框架，涵盖物理驱动的最大似然估计（MLE）方法和数据驱动的图神经网络（GNN）。传统MLE框架整合了空间等时击中选择、通过时间残差M估计器最小化的顶点重建，以及基于预先计算的光子分布表的解耦方向与能量估计；似然公式中纳入了光电倍增管（PMT）角接收度、击中级时间游动修正和有效线源簇射扩展等物理校准。并行开发了两阶段GNN，以捕捉DOM内PMT相关性和距离加权的DOM间拓扑模式。模拟研究表明，在1 TeV至1 PeV能量范围内，MLE方法的整体中值角分辨率为4.19°，能量分辨率为25%-37%，系统偏差可忽略；GNN进一步提升了中低能区的重建保真度，在30 TeV时中值角分辨率达1.8°，在40至300 TeV之间能量分辨率约为20%。基于这些重建性能，评估了NEON的有效面积和点源发现潜力。该框架为NEON建立了必要的重建基准，并为未来下一代深海中微子望远镜提供了实用方法。
英文摘要
The Neutrino Observatory in the Nanhai (NEON) is a proposed deep-sea neutrino telescope deployed in the South China Sea. Accurate reconstruction of shower-like events is crucial for neutrino energy measurements and multi-messenger astronomy, yet it poses significant challenges due to seawater optical attenuation, irregular detector geometry, and substantial $^{40}\mathrm{K}$ ambient background. In this work, we present the first comprehensive reconstruction framework for shower-like events in NEON, encompassing both a physics-driven maximum likelihood estimation (MLE) method and a data-driven Graph Neural Network (GNN). The traditional MLE framework integrates spatial-isochronic hit selection, vertex reconstruction via time-residual M-estimator minimization, and decoupled directional and energy estimation based on pre-computed photon distribution tables. Physical calibrations, including PMT angular acceptance, hit-level time slewing corrections, and an effective line-source shower extension, are incorporated into the likelihood formulation. In parallel, a two-stage GNN is developed to capture intra-DOM PMT correlations and distance-weighted inter-DOM topological patterns. Simulation studies show that the MLE method achieves an overall median angular resolution of $4.19^\circ$ and an energy resolution of 25\%-37\% over 1 TeV to 1 PeV with negligible systematic bias. The GNN further improves reconstruction fidelity in the low-to-intermediate energy regime, achieving a median angular resolution of $1.8^\circ$ at 30 TeV and an energy resolution of $\sim$ 20\% between 40 and 300 TeV. Based on these reconstruction performances, the effective area and point-source discovery potential of NEON are evaluated. This framework establishes an essential reconstruction benchmark for NEON and provides practical methodologies for future next-generation deep-sea neutrino telescopes.
