Decay-aware neural network for event classification in collider physics
Decay-aware neural network for event classification in collider physics
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发表时间：
2022-12
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通讯作者：
T. Kishimoto;M. Morinaga;M. Saito;J. Tanaka
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作者：
T. Kishimoto;M. Morinaga;M. Saito;J. Tanaka
对撞机物理中事件分类的目标是将感兴趣的信号事件与背景事件区分开来，以尽可能地寻找自然界中的新现象。我们提出了一种基于多任务学习技术的衰减感知神经网络来有效地解决这种事件分类问题。该模型将粒子衰变的领域知识作为辅助任务进行学习，为提高事件分类的学习效率提供了一种新的途径。使用模拟数据的实验证实，通过添加辅助任务，成功地引入了归纳偏差，并且与Boost决策树和简单的多层感知器模型相比，事件分类获得了显著的改善。
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.