Methods for processing point cloud information have seen a great success in collider physics applications. One recent breakthrough in machine learning is the usage of transformer networks to learn semantic relationships between sequences in language processing. In this work, we apply a modified transformer network called point cloud transformer as a method to incorporate the advantages of the transformer architecture to an unordered set of particles resulting from collision events. To compare the performance with other strategies, we study jet-tagging applications for highly-boosted particles.

中文翻译：

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点云变换器应用于对撞机物理

处理点云信息的方法在对撞机物理应用中取得了巨大成功。最近机器学习的一项突破是使用转换器网络来学习语言处理中序列之间的语义关系。在这项工作中，我们应用了一种称为点云变换器的改进变换器网络，将变换器架构的优点结合到由碰撞事件产生的一组无序粒子中。为了将性能与其他策略进行比较，我们研究了高度增强粒子的喷射标记应用。