{
  "abstract": "In this work, we study the single production of a vector-like singlet top partner \\( T \\) at the 14 TeV HL-LHC in the channel \\( pp \\to T j \\) with \\( T \\to t Z \\), \\( t \\to b W \\to b j j \\), and \\( Z \\to ν\\barν \\). Signal and background samples are generated with MadGraph5\\_aMC@NLO v3.5.11, showered with Pythia 8, and passed through Delphes. The dominant backgrounds are \\( t \\bar{t} \\), \\( t Z j \\), \\( ZZ j j \\), and \\( W Z j j \\) (including charge conjugates). A hadronic pre-selection (\\( N_j \\geq 3 \\), \\( N_b \\geq 1 \\), \\( N_\\ell = 0 \\)) is imposed as trigger, followed by optimized kinematic cuts. We perform multivariate classification with Extreme Gradient Boosting (XGBoost) and a Graph Neural Network (GNN) based on jet-level features. Sensitivities at 3000 fb\\(^{-1}\\) are quoted using the Asimov significance, \\( S / \\sqrt{S + B} \\), and an Asimov variant with a 20\\% background systematic. The model parameters \\( g^* \\) and \\( R_L \\) are defined in Sec.~2, and a single global working point is used to avoid per-mass tuning bias. In the \\( (g^*, m_T) \\) scan, we present 2\\(σ\\) exclusion and 5\\(σ\\) discovery contours for \\( R_L = 0 \\) and \\( R_L = 0.5 \\). For \\( R_L = 0 \\), 2\\(σ\\) exclusion corresponds to \\( g^* \\in [0.17, 0.49] \\) (\\( 0.16, 0.43 \\)) over \\( m_T \\in [1.8, 2.7] \\) TeV, while 5\\(σ\\) discovery corresponds to \\( g^* \\in [0.27, 0.44] \\) (\\( 0.26, 0.40 \\)) over \\( m_T \\in [1.8, 2.2] \\) TeV for XGBoost and GNN respectively. For \\( R_L = 0.5 \\), the 2\\(σ\\) reach is \\( g^* \\in [0.21, 0.48] \\) (\\( 0.20, 0.43 \\)) over \\( m_T \\in [1.8, 2.5] \\) TeV, and the 5\\(σ\\) reach is \\( g^* \\in [0.33, 0.43] \\) (\\( 0.31, 0.49 \\)) over \\( m_T \\in [1.8, 2.2] \\) TeV, with the GNN yielding slightly stronger and smoother limits across the scan.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2606.03913",
  "author_total": 5,
  "authors": [
    {
      "name": "Haroon Sagheer"
    },
    {
      "name": "M. Tayyab Javaid"
    },
    {
      "name": "Ijaz Ahmed"
    },
    {
      "name": "Ali Hassan"
    },
    {
      "name": "Farzana Ahmad"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/010swpd825dqc.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2606.03913",
  "fulltext_url": null,
  "id": "010swpd825dqc",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2606.03913",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "hep-ph"
    ],
    [
      "openalex",
      "3106|Particle physics theoretical and experimental studies"
    ]
  ],
  "openalex": "W7163379847",
  "pdf_url": "https://arxiv.org/pdf/2606.03913",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-11",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/010swpd825dqc.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:06Z",
      "native_id": "2606.03913",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2606.03913"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:hep-ph",
      "subfield": "high-energy"
    }
  ],
  "title": "Probing Singlet Vector-Like Top Quarks in the Hadronic tZ Channel at the HL-LHC using Machine and Deep Learning Architectures",
  "title_text": "Probing Singlet Vector-Like Top Quarks in the Hadronic tZ Channel at the HL-LHC using Machine and Deep Learning Architectures",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:06Z",
  "url": "https://research.draconitic.com/paper/010swpd825dqc",
  "venue": "arXiv",
  "versions": []
}