Convolutional neural networks (CNNs) have seen extensive applications in scientific data analysis, including in neutrino telescopes. However, the data from these experiments present numerous challenges to CNNs, such as non-regular geometry, sparsity, and high dimensionality. Consequently, CNNs are highly inefficient on neutrino telescope data, and require significant pre-processing that results in information loss. We propose sparse submanifold convolutions (SSCNNs) as a solution to these issues and show that the SSCNN event reconstruction performance is comparable to or better than traditional and machine learning algorithms. Additionally, our SSCNN runs approximately 16 times faster than a traditional CNN on a GPU. As a result of this speedup, it is expected to be capable of handling the trigger-level event rate of IceCube-scale neutrino telescopes. These networks could be used to improve the first estimation of the neutrino energy and direction to seed more advanced reconstructions, or to provide this information to an alert-sending system to quickly follow-up interesting events.

翻译：卷积神经网络（CNN）在科学数据分析中已得到广泛应用，其中包括中微子望远镜领域。然而，这些实验数据给CNN带来了诸多挑战，如非规则几何结构、稀疏性和高维度。因此，CNN在中微子望远镜数据上效率极低，且需要大量预处理步骤，导致信息损失。我们提出使用稀疏子流形卷积神经网络（SSCNN）来解决这些问题，并表明SSCNN的事件重建性能可与传统及机器学习算法相媲美甚至更优。此外，我们的SSCNN在GPU上的运行速度约为传统CNN的16倍。凭借这一速度优势，它有望处理冰立方尺度中微子望远镜的触发级事件率。这些网络可用于改进中微子能量和方向的初步估计，为更高级的重建提供初始条件，或向警报发送系统提供信息以快速跟进有趣的事件。