{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.05104",
  "author_total": 4,
  "authors": [
    {
      "name": "Sanchari Bhattacharyya"
    },
    {
      "name": "Biplob Bhattacherjee"
    },
    {
      "name": "Kirana K. K."
    },
    {
      "name": "Swagata Mukherjee"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fkkahjcb8vgd0.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.05104",
  "fulltext_url": null,
  "id": "fkkahjcb8vgd0",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.05104",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "hep-ex"
    ],
    [
      "arxiv",
      "hep-ph"
    ],
    [
      "openalex",
      "3106|Particle physics theoretical and experimental studies"
    ]
  ],
  "openalex": "W7220718602",
  "pdf_url": "https://arxiv.org/pdf/2610.05104",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-04",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fkkahjcb8vgd0.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:01Z",
      "native_id": "2610.05104",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.05104"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:hep-ex",
      "subfield": "high-energy"
    }
  ],
  "title": "Boosted top tagging via lepton-in-jet topology for vector-like quark searches in FCC-hh",
  "title_text": "Boosted top tagging via lepton-in-jet topology for vector-like quark searches in FCC-hh",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:01Z",
  "url": "https://research.draconitic.com/paper/fkkahjcb8vgd0",
  "venue": "arXiv",
  "versions": []
}