arXiv 2212.024422022-12hep-phhep-ex

机器学习增强的对衰变到单态标量或赝标量的矢量类单态 $B$ 夸克的搜寻

Machine learning-enhanced search for a vectorlike singlet $B$ quark decaying to a singlet scalar or pseudoscalar

Jai Bardhan, Tanumoy Mandal, Subhadip Mitra, Cyrin Neeraj

备注：: 15 pages, 12 figures and 2 tables. Article matches the published version
发表信息：: Phys. Rev. D 107 (2023), 115001
DOI：: 10.1103/PhysRevD.107.115001

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AI总结：

本文提出利用含轻子的混合衰变末态 $BB\to(b\Phi)(tW)$ 和加权交叉熵深度神经网络，在高亮度LHC上搜寻衰变到单态标量或赝标量的矢量类 $B$ 夸克，并评估参数空间发现与排除范围。

摘要：

新衰变模式的存在显著放宽了当前对矢量类夸克的质量排除限制。我们考虑弱单态矢量类 $B$ 夸克可衰变到单态标量或赝标量 $\Phi$ 的情形。在早前的一篇论文中[A. Bhardwaj 等人，探索衰变到新标量或赝标量的矢量类夸克的路线图，Phys. Rev. D, 106 (2022) 095014；arXiv:2203.13753]，我们梳理了在 LHC 上探索此类设置的可能性。我们证明了，$B$ 夸克可能衰变到 $\Phi$，而 $\Phi$ 在无需对参数进行微调的情况下主要衰变到一对胶子或 $b$ 夸克。在本文中，我们提出了一种对撞机搜寻策略，以寻找单态 $B$ 夸克的成对产生。如果两个 $B$ 夸克都衰变为 $b\Phi$ 对，则末态完全是强子性的：$B{B}\to(b\Phi)({b}\Phi)\to (bgg)({b}gg)/(bb{b})({b}b{b})$，这非常难以探测。因此，我们考虑单态 $B$ 情形特有的更简单的混合衰变 $BB\to(b\Phi)(tW)$，其末态包含一个轻子，以在高亮度 LHC 上获得最佳灵敏度。我们使用带有加权交叉熵损失的深度神经网络将信号与巨大的标准模型（SM）背景分离。我们的分析表明，$M_{B}-M_\Phi$ 参数空间的大片区域可通过这一特征被发现。我们展示了发现和排除区域如何随新衰变模式的分支比而变化。我们还使用相同的网络模型估计了在包含性单轻子通道中的可达范围。

英文摘要：

The presence of a new decay mode relaxes the current mass exclusion limits on vectorlike quarks considerably. We consider the case of a weak-singlet vectorlike $B$ quark that can decay to a singlet scalar or pseudoscalar $Φ$. In an earlier paper [A. Bhardwaj et al., Roadmap to explore vectorlike quarks decaying to a new scalar or pseudoscalar, Phys. Rev. D, 106 (2022) 095014; arXiv:2203.13753], we mapped the possibilities to explore such setups at the LHC. We showed that it is possible for a $B$ quark to decay into $Φ$ and the $Φ$ to dominantly decay to a pair of gluons or $b$ quark(s) without fine-tuning the parameters. In this paper, we present a collider search strategy to look for the pair production of singlet $B$ quarks. If both $B$ quarks decay into $bΦ$ pairs, the final state is fully hadronic: $B{B}\to(bΦ)({b}Φ)\to (bgg)({b}gg)/(bb{b})({b}b{b})$, which is very challenging to probe. Therefore, we consider a simpler mixed decay specific to the singlet $B$ case, $BB\to(bΦ)(tW)$ with a lepton in the final state, to achieve the best sensitivity at the high-luminosity LHC. We use a deep neural network with a weighted cross-entropy loss to separate the signal from the huge SM background. Our analysis shows that large areas of the $M_{B}-M_Φ$ parameter space are discoverable through this signature. We show how the discovery and exclusion regions scale with the branching ratio in the new decay mode. We also estimate the reach in the inclusive monolepton channel with the same network model.

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