论文标题
对撞机物理事件分类的衰减感知的神经网络
Decay-aware neural network for event classification in collider physics
论文作者
论文摘要
对撞机物理学中事件分类的目的是将感兴趣的信号事件从背景事件区分开，从而可能在自然界中寻找新现象。我们建议基于多任务学习技术的衰减感知神经网络，以有效解决此事件分类。所提出的模型旨在将粒子衰减的领域知识作为辅助任务学习，这是提高事件分类学习效率的一种新方法。我们使用仿真数据的实验证实，通过添加辅助任务成功引入了归纳偏置，并且与增强的决策树和简单的多层感知器模型相比，事件分类的显着改进得到了显着改进。
The goal of event classification in collider physics is to distinguish signal events of interest from background events to the extent possible to search for new phenomena in nature. We propose a decay-aware neural network based on a multi-task learning technique to effectively address this event classification. The proposed model is designed to learn the domain knowledge of particle decays as an auxiliary task, which is a novel approach to improving learning efficiency in the event classification. Our experiments using simulation data confirmed that an inductive bias was successfully introduced by adding the auxiliary task, and significant improvements in the event classification were achieved compared with boosted decision tree and simple multi-layer perceptron models.
