用图神经网络识别底夸克新粒子的全喷注衰变信号
Tagging fully hadronic exotic decays of the vectorlike $\mathbf{B}$ quark using a graph neural network
- 结合图神经网络与深度网络，挖掘复杂喷注结构特征
- 在全喷注信号下实现接近半轻子模式的探测能力
- 适合高能物理中寻找新奇粒子的机器学习研究者
在之前工作基础上，我们研究了大型强子对撞机（LHC）中成对产生、以全新规范单态（赝）标量场Φ和底夸克形式完全外延衰变的矢量型B夸克的探测前景。电弱对称性破缺后，Φ主要衰变为gg或bb末态，形成2b+4j或6b的全喷注信号。由于标准模型背景大且无轻子道，探测极具挑战。为此，我们采用包含图神经网络与深度神经网络的混合深度学习模型。估算表明，该先进分析流程可使探测性能媲美半轻子模式，在全外延衰变（BR(B→bΦ)=100%）条件下，高亮度LHC的发现（排除）灵敏度可达约1.8（2.4）TeV。
原文摘要 · Abstract (English)
Following up on our earlier study in [J. Bardhan et al., Machine learning-enhanced search for a vectorlike singlet B quark decaying to a singlet scalar or pseudoscalar, Phys. Rev. D 107 (2023) 115001; arXiv:2212.02442], we investigate the LHC prospects of pair-produced vectorlike $B$ quarks decaying exotically to a new gauge-singlet (pseudo)scalar field $Φ$ and a $b$ quark. After the electroweak symmetry breaking, the $Φ$ decays predominantly to $gg/bb$ final states, leading to a fully hadronic $2b+4j$ or $6b$ signature. Because of the large Standard Model background and the lack of leptonic handles, it is a difficult channel to probe. To overcome the challenge, we employ a hybrid deep learning model containing a graph neural network followed by a deep neural network. We estimate that such a state-of-the-art deep learning analysis pipeline can lead to a performance comparable to that in the semi-leptonic mode, taking the discovery (exclusion) reach up to about $M_B=1.8\:(2.4)$ TeV at HL-LHC when $B$ decays fully exotically, i.e., BR$(B \to bΦ) = 100\%$.
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