In high energy physics, graph-based implementations have the advantage of treating the input data sets in a similar way as they are collected by collider experiments. To expand on this concept, we propose a graph neural network enhanced by attention mechanisms called ABCNet. To exemplify the advantages and flexibility of treating collider data as a point cloud, two physically motivated problems are investigated: quark–gluon discrimination and pileup reduction. The former is an event-by-event classification, while the latter requires each reconstructed particle to receive a classification score. For both tasks, ABCNet shows an improved performance compared to other algorithms available.

在高能物理中，基于图的实现方式具有以与对撞机实验收集的输入数据集相似的方式处理输入数据集的优势。为了扩展这个概念，我们提出了一种通过称为ABCNet的注意力机制增强的图神经网络。为了证明将对撞机数据视为点云的优势和灵活性，研究了两个物理动机问题：夸克-胶子判别和堆积减少。前者是一个逐个事件的分类，而后者则要求每个重建的粒子都要接收一个分类分数。对于这两项任务，与其他可用算法相比，ABCNet均显示出更高的性能。