AI 中文总结
本研究采用图神经网络分类方法，探究ESSνSB近水切伦科夫探测器的味识别潜力，发现其体积缩小8倍仍可保持较高分类性能，减少光电倍增管覆盖的影响有限，效率损失可通过增加曝光时间补偿。
AI 中文摘要
ESSνSB实验旨在高精度测量轻子领域的电荷共轭宇称（CP）破坏，这需要对水切伦科夫（WC）探测器中的中微子事例进行可靠重建。本研究采用基于图神经网络（GNN）的分类方法，探究拟议的近水切伦科夫探测器的味识别潜力，重点关注关键探测器设计参数的变化。我们特别研究更小和/或更少仪器化的探测器是否能达到所需的分类性能。通过对带电电流（CC）中微子相互作用进行详细的蒙特卡罗模拟，我们训练GNN分类器以区分电子中微子和缪子中微子的带电电流事例。研究发现，即使探测器体积比标称设计小8倍，基于GNN的分类仍然保持准确，在固定背景排斥下分类效率仅出现适度下降。这种效率损失可通过增加曝光时间在很大程度上得到补偿。此外，我们证明，只要在信号产额最高的区域（尤其是前向端盖附近）保持光电倍增管（PMT）的覆盖，标称探测器中减少PMT覆盖对分类性能的影响有限。
英文摘要
The ESS$ν$SB experiment aims to measure CP violation in the leptonic sector with high precision, necessitating robust reconstruction of neutrino events in the water Cherenkov (WC) detectors. In this work, we investigate the flavour identification potential of the proposed near WC detector using graph neural network (GNN)-based classification, with a focus on variations of key detector design parameters. In particular, we study whether a smaller and/or less instrumented detector can achieve the required classification performance. Using detailed Monte Carlo simulations of charged-current (CC) neutrino interactions, we train GNN classifiers to distinguish electron and muon neutrino CC events. We find that GNN-based classification remains accurate even for detector configurations with volumes up to a factor of eight smaller than the nominal design, with only moderate degradation in classification efficiency at fixed background rejection. The resulting loss in efficiency can largely be compensated by increased exposure time. Furthermore, we demonstrate that reduced photomultiplier tube (PMT) coverage in the nominal detector has a limited impact on classification performance, provided that coverage is maintained in regions of highest signal yield, in particular near the forward end-cap.
Comments23 pages, 22 figures
