论文标题
具有两点能量相关性和软排放几何形状的基于神经网络的顶级标签器
Neural Network-based Top Tagger with Two-Point Energy Correlations and Geometry of Soft Emissions
论文作者
论文摘要
经过喷气图像训练的深度神经网络已成功地对不同的喷气机进行了分类。在本文中，我们确定了可以重现顶级喷气机与QCD Jet分类中卷积神经网络的分类性能的关键物理特征。我们设计了一个神经网络，该神经网络考虑了两种类型的子结构特征：两点能量相关性以及射流图像形态学分析的IRC不安全计数变量。新的IRC不安全变量集可以由积分几何形状的Minkowski函数描述。为了将这些功能集成到单个框架中，我们根据图神经网络重新引入了两点能量相关性，然后为网络提供其他功能。该网络显示出与卷积神经网络的可比分类性能。由于两个网络在某种程度上都使用IRC不安全功能，因此基于模拟的结果通常取决于事件生成器的选择。我们比较了Pythia 8和Herwig 7的分类结果，并且对IRC不安全特征的分布进行了简单的重新加权，这减少了两个模拟结果之间的差异。
Deep neural networks trained on jet images have been successful in classifying different kinds of jets. In this paper, we identify the crucial physics features that could reproduce the classification performance of the convolutional neural network in the top jet vs. QCD jet classification. We design a neural network that considers two types of substructural features: two-point energy correlations, and the IRC unsafe counting variables of a morphological analysis of jet images. The new set of IRC unsafe variables can be described by Minkowski functionals from integral geometry. To integrate these features into a single framework, we reintroduce two-point energy correlations in terms of a graph neural network and provide the other features to the network afterward. The network shows a comparable classification performance to the convolutional neural network. Since both networks are using IRC unsafe features at some level, the results based on simulations are often dependent on the event generator choice. We compare the classification results of Pythia 8 and Herwig 7, and a simple reweighting on the distribution of IRC unsafe features reduces the difference between the results from the two simulations.
