论文标题
ABCNET：一种基于注意粒子标记的方法
ABCNet: An attention-based method for particle tagging
论文作者
论文摘要
在高能物理学中，基于图的实现具有与对撞机实验收集的相似方式处理输入数据集的优点。为了扩展这一概念，我们提出了一个被称为ABCNET的注意机制增强的图形神经网络。为了体现将对撞机数据视为点云的优势和灵活性，研究了两个出于物理动机的问题：夸克 - 杜伦歧视和减少堆积。前者是事件的分类，而后者需要每个重建的粒子才能获得分类分数。对于这两个任务，与可用的其他算法相比，ABCNET均显示出改进的性能。
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.
