发表机构
Centre for High Energy Physics, Indian Institute of Science; Department of Physics, Indian Institute of Technology, Kanpur（印度科学学院高能物理中心; 坎普尔印度理工学院物理系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出机器学习框架，利用轻子-in-jet拓扑标记增强顶夸克，用于FCC-hh上矢量样B'夸克搜索，实现约7-10 TeV的5σ发现能力。
AI 中文摘要
在本文中，我们提出了一种基于机器学习的框架，用于在未来环形对撞机：强子-强子模式（FCC-hh）中，以质心能量$\sqrt{s}=100$~TeV运行条件下，识别轻子衰变、高度增强的顶夸克，$t\to b\ell\nu$。在这些能量下，大的洛伦兹增强导致顶夸克衰变产物高度共线，轻子嵌入在产生的顶夸克喷注中。在此情况下，尚不清楚基于成分的喷注标记器（依赖于单个喷注成分的信息）是否能保持LHC能量下实现的高区分能力。为回答此问题，我们采用两种互补方法：一种基于变量的标记器，使用深度神经网络（DNN）训练，利用物理驱动的观测量；另一种基于成分的粒子变换器（ParT），作用于喷注成分。两种方法均针对增强轻子顶衰变的特征性非隔离轻子信号进行优化，并在区分轻子顶喷注与强子顶喷注及重味$b$-和$c$-喷注背景方面实现相当的性能。然后，我们将标记框架整合到事件级搜索中，用于单产生矢量样$B^\prime$夸克，通过$B^\prime\to tW$衰变，并用于评估在$3$至$10$~TeV范围内对$B^\prime$的灵敏度。对于参考$B^\prime$基准，所提出的搜索策略在宽度要求$\Gamma_{B^\prime}/m_{B^\prime}<25\\%$下，在仅统计和$10\\%$背景系统不确定性两种情景中，均实现约$7$~TeV的$5\sigma$发现能力。将宽度要求放宽至$\Gamma_{B^\prime}/m_{B^\prime}\lesssim 50\\%$，发现能力扩展至约$10$~TeV。
英文摘要
In this paper, we present a machine-learning-based framework for identifying leptonically decaying, highly boosted top quarks, $t\to b\ellν$, at the Future Circular Collider: hadron-hadron mode (FCC-hh) operating at a center-of-mass energy of $\sqrt{s}=100$~TeV. At these energies, the large Lorentz boost causes the top-quark decay products to become highly collimated, with the lepton embedded within the resulting top-quark jet. In this regime, it is not a priori clear whether constituent-based jet taggers, which rely on information from individual jet constituents, retain the high discrimination power achieved at LHC energies. To answer this question, we employ two complementary approaches: a variable-based tagger trained with a deep neural network (DNN) using physics-motivated observables and a constituent-based Particle Transformer (ParT) acting on the jet constituents. Both approaches are optimised for the characteristic non-isolated lepton signature of boosted leptonic top decays and achieve comparable performance in discriminating leptonic top jets from hadronic top jets and heavy-flavor $b$- and $c$-jet backgrounds. We then incorporate the tagging framework into an event-level search for single production of a vector-like $B^\prime$ quark decaying through $B^\prime\to tW$, and use it to evaluate sensitivity to $B^\prime$ in the range of $3$ to $10$~TeV. For the reference $B^\prime$ benchmark, the proposed search strategy achieves a $5σ$ discovery reach of approximately $7$~TeV under the width requirement $Γ_{B^\prime}/m_{B^\prime}<25\%$, in both the statistics-only and $10\%$ background systematic uncertainty scenarios. Relaxing the width requirement to $Γ_{B^\prime}/m_{B^\prime}\lesssim 50\%$ extends the discovery reach to approximately $10$~TeV.
Comments40 pages, 23 figures, 9 tables
