Boosted top tagging via lepton-in-jet topology for vector-like quark searches in FCC-hh

Abstract

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.