用双模型集成提升喷注分类准确率，助力新物理探索
Jet Image Tagging Using Deep Learning: An Ensemble Model
- 将喷注转为二维直方图，双神经网络协同学习特征
- 在JetNet数据集上实现顶夸克、轻夸克等多类喷注识别
- 集成模型性能优于单个网络，适合高能物理分类任务
高能粒子物理中的喷注分类对于理解基本相互作用及探测标准模型以外的现象至关重要。喷注源于夸克和胶子的碎裂与强子化，因其复杂的多维结构而难以识别。传统方法难以捕捉其细节，亟需先进机器学习手段。本文采用两个神经网络组成的集成模型，将喷注数据转化为二维直方图而非高维点表示，用于对JetNet数据集中的喷注类型进行分类，包括顶夸克、轻夸克（上或下）以及W和Z玻色子。该集成模型可实现二分类与多类别分类，通过融合各子网络优势，显著提升性能，优于任一单独网络。
原文摘要 · Abstract (English)
Jet classification in high-energy particle physics is important for understanding fundamental interactions and probing phenomena beyond the Standard Model. Jets originate from the fragmentation and hadronization of quarks and gluons, and pose a challenge for identification due to their complex, multidimensional structure. Traditional classification methods often fall short in capturing these intricacies, necessitating advanced machine learning approaches. In this paper, we employ two neural networks simultaneously as an ensemble to tag various jet types. We convert the jet data to two-dimensional histograms instead of representing them as points in a higher-dimensional space. Specifically, this ensemble approach, hereafter referred to as Ensemble Model, is used to tag jets into classes from the JetNet dataset, corresponding to: Top Quarks, Light Quarks (up or down), and W and Z bosons. For the jet classes mentioned above, we show that the Ensemble Model can be used for both binary and multi-categorical classification. This ensemble approach learns jet features by leveraging the strengths of each constituent network achieving superior performance compared to either individual network.
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