发表机构
Ecole Polytechnique, IN2P3-CNRS, Laboratoire Leprince-Ringuet; Kavli Institute for the Physics and Mathematics of the Universe (WPI), The University of Tokyo Institutes for Advanced Study, University of Tokyo（巴黎综合理工学院，法国国家科学研究中心IN2P3，勒普朗斯-林格特实验室; 东京大学宇宙线起源研究机构（WPI），东京大学高等研究院，东京大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究将图神经网络（GNN）用于Hyper-Kamiokande类水切伦科夫探测器的弥散超新星中微子背景（DSNB）探测，开发了能量恢复模型与信号-本底分类器，性能达当前最先进水平。
AI 中文摘要
对弥散超新星中微子背景（DSNB）的研究是加深对核心坍缩超新星形成理解以及探究中微子基本性质的重要途径。然而，DSNB通量的探测是一项艰巨任务，因为需要在低能区将其信号与众多竞争本底区分开。DSNB的主要探测通道是逆β衰变（IBD），其特征为具有明显的、时间分离的双信号特征。本研究引入图神经网络（GNN）来处理通过IBD通道进行DSNB探测和重建的关键任务，具体而言，我们提出了一个从低能正电子中恢复第一个信号能量的模型，此外，我们开发了一个针对次级信号优化的信号-本底分类器，该次级信号对应于中子俘获事件后的特征光子发射。我们在类似 Hyper-Kamiokande 的水切伦科夫探测器几何结构的两个模拟中测试了我们的模型，结果表明，我们获得的性能可与当前最先进水平相媲美。
英文摘要
The study of the Diffuse Supernova Neutrino Background (DSNB) is an essential approach to improve our understanding of the formation of Core-Collapse Supernovae, as well as investigating fundamental neutrino properties. The detection of the DSNB flux however is a difficult task, as it requires the disentanglement of its signal with many competitive backgrounds in the low energy range. The main DSNB detection channel is the Inverse Beta Decay (IBD), which is characterised by a distinct, time-separated two-signal signature. This work introduces the use of Graph Neural Networks (GNNs) to handle critical detection and reconstruction tasks for the DSNB through the IBD channel. In particular, we present a model to recover the energy of the first signal from a low energy positron. Additionally, we develop a signal-to-background classifier optimised for the secondary signal, which corresponds to the characteristic photon emission following a neutron capture event. We test our models on two simulations in a Hyper-Kamiokande-like water Cherenkov detector geometry, and show that we obtain performances that are comparable to the state of the art.
