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arXiv 1803.08066hep-phstat.ML
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Jet Charge和机器学习

Jet Charge and Machine Learning

Katherine Fraser, Matthew D. Schwartz

备注：
17 pages, 8 figures, 1 table; Updated to JHEP version
发表信息：
JHEP10 (2018) 093
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摘要：

现代机器学习技术，如卷积、递归和递归神经网络，已经显示出在大型强子对撞机上实现喷流子结构的希望。例如，他们已经证明了在提升顶部或W玻色子识别或夸克/胶子歧视的有效性。我们探索这些方法的目的是根据它们的电荷对射流进行分类。我们发现，将射流内的距离作为输入的神经网络和包括径向距离信息的提升决策树可以提供比当前方法更显著的射流电荷提取改进。具体来说，卷积、递归和递归网络可以提供比传统方法更大的改进，部分原因是有效地利用了射流或聚类历史中的距离。使用固定大小的输入表示（如CNN）或小输入表示（如RNN）的优势表明，卷积和递归网络对于碰撞机上现代机器学习的未来至关重要。

英文摘要：

Modern machine learning techniques, such as convolutional, recurrent and recursive neural networks, have shown promise for jet substructure at the Large Hadron Collider. For example, they have demonstrated effectiveness at boosted top or W boson identification or for quark/gluon discrimination. We explore these methods for the purpose of classifying jets according to their electric charge. We find that both neural networks that incorporate distance within the jet as an input and boosted decision trees including radial distance information can provide significant improvement in jet charge extraction over current methods. Specifically, convolutional, recurrent, and recursive networks can provide the largest improvement over traditional methods, in part by effectively utilizing distance within the jet or clustering history. The advantages of using a fixed-size input representation (as with the CNN) or a small input representation (as with the RNN) suggest that both convolutional and recurrent networks will be essential to the future of modern machine learning at colliders.

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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