Tasks involving the analysis of geometric (graph- and manifold-structured)
data have recently gained prominence in the machine learning community, giving
birth to a rapidly developing field of geometric deep learning. In this work,
we leverage graph neural networks to improve signal detection in the IceCube
neutrino observatory. The IceCube detector array is modeled as a graph, where
vertices are sensors and edges are a learned function of the sensors' spatial
coordinates. As only a subset of IceCube's sensors is active during a given
observation, we note the adaptive nature of our GNN, wherein computation is
restricted to the input signal support. We demonstrate the effectiveness of our
GNN architecture on a task classifying IceCube events, where it outperforms
both a traditional physics-based method as well as classical 3D convolution
neural networks.

本文提出了一种基于图神经网络的信号检测算法，将冰立方探测器阵列建模成图形，通过学习传感器间的空间坐标构建边，实现仅针对输入信号支持计算的自适应计算，并在对冰立方探测器事件分类任务中展示了其优越性。