粒子物理学中的图形神经网络：实现，创新和挑战
Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges
摘要：
许多物理系统可以被最好地理解为具有关联关系的离散数据集。在以前，这些数据集已经被公式化为系列或图像数据以匹配可用的机器学习架构，随着图神经网络（GNN）的出现，这些系统可以原生地学习为图。这允许将各种各样的高级和低级物理特征附加到测量中，并且同样地，通过相同的GNN架构来完成各种各样的HEP任务。GNN在重建、标记、生成和端到端分析方面都有强大的用例。随着GNN在工业中的广泛采用，HEP社区可以从GNN延迟和内存使用的快速改善中受益。然而，行业用例与HEP并不完全一致，需要做很多工作才能使独特的GNN功能与独特的HEP障碍最佳匹配。我们在这里提出了一系列这些能力，预测目前正在采用的HEP社区，这仍然是不成熟的。我们希望能够捕捉到机器学习中图形技术的全貌，并指出阻碍研究取得巨大飞跃的最重要的差距。
英文摘要：
Many physical systems can be best understood as sets of discrete data with associated relationships. Where previously these sets of data have been formulated as series or image data to match the available machine learning architectures, with the advent of graph neural networks (GNNs), these systems can be learned natively as graphs. This allows a wide variety of high- and low-level physical features to be attached to measurements and, by the same token, a wide variety of HEP tasks to be accomplished by the same GNN architectures. GNNs have found powerful use-cases in reconstruction, tagging, generation and end-to-end analysis. With the wide-spread adoption of GNNs in industry, the HEP community is well-placed to benefit from rapid improvements in GNN latency and memory usage. However, industry use-cases are not perfectly aligned with HEP and much work needs to be done to best match unique GNN capabilities to unique HEP obstacles. We present here a range of these capabilities, predictions of which are currently being well-adopted in HEP communities, and which are still immature. We hope to capture the landscape of graph techniques in machine learning as well as point out the most significant gaps that are inhibiting potentially large leaps in research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
