{
  "abstract": "A study of the discovery potential for a CP-even heavy neutral Higgs boson $H$ is presented in the Type-I two-Higgs-doublet model (2HDM) at a future $e^+e^-$ collider with $\\sqrt{s}=500~\\mathrm{GeV}$, focusing on the process $e^+e^-\\to ZH$ with $H\\to WW$, and with $Z\\to\\ell\\ell$ or $Z\\toνν$. The final states are four jets plus two leptons ($4j2\\ell$) and four jets plus missing transverse momentum ($4j2ν$). Both a Boosted Decision Tree (BDT) and a Deep Neural Network (DNN) were trained on the same inputs. The BDT was found to be substantially faster in training and inference without any loss of performance, and it is therefore the classifier used in the interpretation. Three strategies are compared: a cut-based analysis, a one-dimensional BDT selection trained per mass point, and a two-dimensional (BDT $\\times$ $m_H$) template analysed with a profile likelihood. The 2D template improves the expected discovery significance by a factor of $2.5$--$3.6$ in the $4j2\\ell$ channel and by a factor of $5$--$18$ in the $4j2ν$ channel relative to the cut-based analysis, with the largest gains at high $m_H$ where the cut-based mass window loses efficiency. We translate the expected exclusions into $95\\%$ CL upper limits on $σ\\times$ BR and discuss the implications for the Type-I 2HDM parameter space.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.02987",
  "author_total": 2,
  "authors": [
    {
      "name": "Majid Hashemi"
    },
    {
      "name": "Faezeh Nasirzadeh"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/74a3jgxg2ye24.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.02987",
  "fulltext_url": null,
  "id": "74a3jgxg2ye24",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.02987",
  "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": null,
  "pdf_url": "https://arxiv.org/pdf/2611.02987",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/74a3jgxg2ye24.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:20:59Z",
      "native_id": "2611.02987",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.02987"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:hep-ph",
      "subfield": "high-energy"
    }
  ],
  "title": "Machine-learning discovery potential for a heavy Higgs boson in $e^+e^-\\to ZH$ at a future lepton collider",
  "title_text": "Machine-learning discovery potential for a heavy Higgs boson in e^+e^-→ ZH at a future lepton collider",
  "type": "preprint",
  "updated_at": "2026-11-09T00:20:39Z",
  "url": "https://research.draconitic.com/paper/74a3jgxg2ye24",
  "venue": "arXiv",
  "versions": []
}