The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches.

中文翻译：

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使用卷积神经网络的 KM3NeT/ORCA 事件重建

KM3NeT 研究基础设施目前正在地中海的两个地点建设。法国海岸附近的 KM3NeT/ORCA 水-切伦科夫中微子探测器将用光电传感器检测数兆吨海水。它的主要目标是确定中微子的质量排序。这项工作旨在展示深度卷积神经网络对中微子望远镜的普遍适用性，以 KM3NeT/ORCA 探测器的模拟数据集为例。为此，网络被用来实现重建和分类任务，这些任务构成了 KM3NeT 意向书中为 KM3NeT/ORCA 提供的分析管道的替代方案。它们用于推断入射中微子的能量、方向和相互作用点的事件重建估计。中微子相互作用中诱发的带电粒子产生的切伦科夫光的空间分布被归类为阵雨状或径迹状，并且识别出与大气中微子探测相关的主要背景过程。提供了与先前为 KM3NeT/ORCA 开发的机器学习分类和最大似然重建算法的性能比较。结果表明，将深度卷积神经网络应用于大体积中微子望远镜的模拟数据集，产生了与经典方法相比具有竞争力的重建结果和性能改进。并识别出与大气中微子探测相关的主要背景过程。提供了与先前为 KM3NeT/ORCA 开发的机器学习分类和最大似然重建算法的性能比较。结果表明，将深度卷积神经网络应用于大体积中微子望远镜的模拟数据集，产生了与经典方法相比具有竞争力的重建结果和性能改进。并识别出与大气中微子探测相关的主要背景过程。提供了与先前为 KM3NeT/ORCA 开发的机器学习分类和最大似然重建算法的性能比较。结果表明，将深度卷积神经网络应用于大体积中微子望远镜的模拟数据集，产生了与经典方法相比具有竞争力的重建结果和性能改进。