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arXiv 2003.11787hep-phhep-ex
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基于神经网络的两点能量相关和软发射几何的Top Tagger

Neural Network-based Top Tagger with Two-Point Energy Correlations and Geometry of Soft Emissions

Amit Chakraborty, Sung Hak Lim, Mihoko M. Nojiri, Michihisa Takeuchi

备注：
43 pages, 25 figures, published in JHEP
发表信息：
J. High Energ. Phys. 2020, 111 (2020)
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摘要：

在喷流图像上训练的深度神经网络已经成功地对不同类型的喷流进行了分类。在本文中，我们确定了关键的物理特征，可以再现卷积神经网络的分类性能在顶部喷流与QCD喷流分类。我们设计了一个神经网络，考虑两种类型的子结构特征：两点能量相关性，和IRC不安全的计数变量的形态分析的射流图像。新的IRC不安全变量集可以用积分几何中的Minkowski泛函来描述。为了将这些特征整合到一个框架中，我们在图神经网络中重新引入了两点能量相关性，并在之后将其他特征提供给网络。该网络显示出与卷积神经网络相当的分类性能。由于这两个网络都在某种程度上使用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，并对IRC不安全特征的分布进行了简单的重新加权，减少了两个模拟结果之间的差异。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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