论文标题
粒子物理中的图神经网络
Graph Neural Networks in Particle Physics
论文作者
论文摘要
粒子物理是科学的一个分支，旨在发现物质和力的基本定律。图形神经网络是可训练的功能，可在图形上运行---元素及其成对关系 - - 是几何深度学习更广泛领域的中心方法。它们非常表现力，并且在各种领域的其他经典深度学习方法上表现出了出色的表现。粒子物理中的数据通常由集合和图表表示，因此，图神经网络提供了关键优势。在这里，我们回顾了粒子物理中图神经网络的各种应用，包括不同的图形结构，模型体系结构和学习目标，以及图形神经网络有希望的粒子物理中的关键开放问题。
Particle physics is a branch of science aiming at discovering the fundamental laws of matter and forces. Graph neural networks are trainable functions which operate on graphs---sets of elements and their pairwise relations---and are a central method within the broader field of geometric deep learning. They are very expressive and have demonstrated superior performance to other classical deep learning approaches in a variety of domains. The data in particle physics are often represented by sets and graphs and as such, graph neural networks offer key advantages. Here we review various applications of graph neural networks in particle physics, including different graph constructions, model architectures and learning objectives, as well as key open problems in particle physics for which graph neural networks are promising.
