arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2008.02831hep-exhep-ph
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神经网络在射流二级顶点搜索中的应用

Secondary Vertex Finding in Jets with Neural Networks

Jonathan Shlomi, Sanmay Ganguly, Eilam Gross, Kyle Cranmer, Yaron Lipman, Hadar Serviansky, Haggai Maron, Nimrod Segol

摘要:

射流分类是粒子冷却器测量和寻找新物理的重要组成部分,二次顶点重建是构建强大射流分类器的关键中间步骤。为了提高分类性能,我们使用神经网络在射流内部执行顶点查找,重点是底部与魅力风味标记的分离。我们实现了一种新的,通用的集图模型,它考虑到信息从所有的轨道在一个喷气机,以确定是否对轨道起源于一个共同的顶点。我们探索不同的性能指标,并找到我们的方法,以优于传统的方法,在准确的二次顶点重建。我们还发现,改进的顶点发现导致射流分类性能的显着改善。

英文摘要:

Jet classification is an important ingredient in measurements and searches for new physics at particle coliders, and secondary vertex reconstruction is a key intermediate step in building powerful jet classifiers. We use a neural network to perform vertex finding inside jets in order to improve the classification performance, with a focus on separation of bottom vs. charm flavor tagging. We implement a novel, universal set-to-graph model, which takes into account information from all tracks in a jet to determine if pairs of tracks originated from a common vertex. We explore different performance metrics and find our method to outperform traditional approaches in accurate secondary vertex reconstruction. We also find that improved vertex finding leads to a significant improvement in jet classification performance.

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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