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arXiv 2412.02468hep-ph
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利用神经网络分离单共振和双共振顶夸克产生过程中左手与反常右手矢量算符对 Wtb 顶点的贡献

Separation of left-handed and anomalous right-handed vector operators contributions into the Wtb vertex for single and double resonant top quark production processes using a neural network

E. Abasov, E. Boos, V. Bunichev, L. Dudko, D. Gorin, A. Markina, M. Perfilov, O. Vasilevskii, P. Volkov, G. Vorotnikov, A. Zaborenko

AI总结:

提出两级深度神经网络方法,对相同 tWb 末态的单、双共振顶夸克事例分类并识别 Wtb 顶点反常右手矢量算符贡献,从而提升搜索灵敏度并设定相空间约束。

摘要:

本文描述了深度神经网络在寻找末态均为 tWb 的单共振和双共振顶夸克产生过程中 Wtb 顶点偏离标准模型预测方面的应用。由第一级神经网络预先分类为对应于单共振或双共振顶夸克产生的 Monte-Carlo 事例,会由两个第二级神经网络进行分析,以判断 Wtb 顶点中是否可能存在反常右手矢量算符的贡献,或这些事例是否对应于标准模型。第二级神经网络对单共振和双共振类别有所不同。这两个类别对反常贡献的依赖方式不同,因此这种拆分能够带来更好的灵敏度。所开发的统计模型用于在相空间的不同区域对 Wtb 顶点处的反常右手矢量算符设定约束。结果表明,所提出的方法能够提高寻找 Wtb 顶点反常贡献的效率。

英文摘要:

The paper describes the application of deep neural networks for the searchdeviations from the Standard Model predictions at the Wtb vertex in the processes of single and double resonant top quark production with identical final state tWb. Monte-Carlo events preliminary classified by first level neural network as corresponding to single or double resonant top quark production are analyzed by two second level neural networks if there is a possible contribution of the anomalous right-handed vector operator into Wtb vertex or events are corresponded to the Standard Model. The second level neural networks are different for single and double resonant classes. The classes depend differently on anomalous contribution and such splitting leads to better sensitivity. The developed statistical model is used to set constraints on the anomalous right-handed vector operator at the Wtb vertex in different regions of phase space. It is demonstrated that the proposed method allows to increase the efficiency of a search for the anomalous contributions to the Wtb vertex.

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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