论文标题
Graphnet：中微子望远镜事件重建的图形神经网络
GraphNeT: Graph neural networks for neutrino telescope event reconstruction
论文作者
论文摘要
Graphnet是一个开源Python框架，旨在提供高质量，用户友好，端到端功能，以使用图形神经网络（GNNS）在中微子望远镜上执行重建任务。 Graphnet使得可以快速且易于训练复杂的模型，这些模型可以提供最新的性能，用于任意检测器配置，其推理时间比传统的重建技术快的阶数。来自Graphernet的GNN足够灵活，可以应用于所有中微子望远镜的数据，包括未来的项目，例如Icecube扩展或P-One。这意味着，基于GNN的重建可用于在中微子望远镜的大多数重建任务上提供最先进的性能，在实时事件，实验和物理分析中，对中微子和天文中微子和天文粒子物理学产生巨大潜在影响。
GraphNeT is an open-source python framework aimed at providing high quality, user friendly, end-to-end functionality to perform reconstruction tasks at neutrino telescopes using graph neural networks (GNNs). GraphNeT makes it fast and easy to train complex models that can provide event reconstruction with state-of-the-art performance, for arbitrary detector configurations, with inference times that are orders of magnitude faster than traditional reconstruction techniques. GNNs from GraphNeT are flexible enough to be applied to data from all neutrino telescopes, including future projects such as IceCube extensions or P-ONE. This means that GNN-based reconstruction can be used to provide state-of-the-art performance on most reconstruction tasks in neutrino telescopes, at real-time event rates, across experiments and physics analyses, with vast potential impact for neutrino and astro-particle physics.
