Experimental particle physics uses machine learning for many of tasks, where one application is to classify signal and background events. The classification can be used to bin an analysis region to enhance the expected significance for a mass resonance search. In natural language processing, one of the leading neural network architectures is the transformer. In this work, an event classifier transformer is proposed to bin an analysis region, in which the network is trained with special techniques. The techniques developed here can enhance the significance and reduce the correlation between the network's output and the reconstructed mass. It is found that this trained network can perform better than boosted decision trees and feed-forward networks.

翻译：实验粒子物理学在众多任务中应用机器学习，其中一项应用是对信号与背景事件进行分类。该分类可用于划分分析区域，以提升质量共振搜索的预期显著性。在自然语言处理领域，Transformer是领先的神经网络架构之一。本研究提出一种事件分类器Transformer来划分分析区域，该网络采用特殊技术进行训练。本文开发的技术能增强显著性，并降低网络输出与重建质量之间的相关性。实验发现，经过训练的网络性能优于提升决策树和前馈神经网络。