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arXiv 1302.1870hep-ph
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学习如何计数:LHC的高多重性搜索

Learning How to Count: A High Multiplicity Search for the LHC

Sonia El Hedri, Anson Hook, Martin Jankowiak, Jay G. Wacker

摘要:

我们介绍了一种搜索技术,是敏感的大终态多重性的信号的广泛的类别。事件被聚类成大半径射流和射流子结构技术被用来计算每个射流内的subjets的数量。搜索包括对事件中的子射流总数以及射流质量和缺失能量的总和的削减。两种不同的技术计数subjets描述和预期的灵敏度为8个基准信号。这些信号表现出不同的现象,包括两步级联衰变,直接三体衰变和多顶末态。我们发现,这些信号的灵敏度提高相比,以前的高多重性搜索,以及减少对丢失的能源需求的依赖。这种方法的一个好处是,它允许自然数据驱动的QCD背景的估计。

英文摘要:

We introduce a search technique that is sensitive to a broad class of signals with large final state multiplicities. Events are clustered into large radius jets and jet substructure techniques are used to count the number of subjets within each jet. The search consists of a cut on the total number of subjets in the event as well as the summed jet mass and missing energy. Two different techniques for counting subjets are described and expected sensitivities are presented for eight benchmark signals. These signals exhibit diverse phenomenology, including 2-step cascade decays, direct three body decays, and multi-top final states. We find improved sensitivity to these signals as compared to previous high multiplicity searches as well as a reduced reliance on missing energy requirements. One benefit of this approach is that it allows for natural data driven estimates of the QCD background.

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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