{
  "abstract": "Right-handed lepton flavor mixing in the left-right symmetric model directly affects the production and decay of heavy Majorana neutrinos $N_R$, yet its impact on collider searches remains less explored. Using a deep neural network (DNN), we analyze the Keung-Senjanović process $pp \\to W_R \\to \\ell_αN_R \\to \\ell_α\\ell_βjj$ with $\\ell_{α,β}=e,μ$ at LHC Run~2 and the HL-LHC, considering both same-sign and opposite-sign dilepton channels. We adopt three benchmark mixing scenarios: unmixed, maximal-mixing, and PMNS-like. In the unmixed scenario, the DNN improves the expected significance over the cut-based analyses performed by ATLAS, leading to stronger expected sensitivities. For the combined $\\ell\\ell$ analysis, the HL-LHC is projected to reach $m_{W_R}$ and $m_{N_R}$ values up to $6.7$ ($6.3$)~TeV and $4.4$ ($4.1$)~TeV, respectively, under maximal (PMNS-like) mixing. For representative mass benchmarks, our analysis indicates that LHC Run~2 can test a significant portion of the $|V_{e1}|\\text{--}|V_{\\mu1}|$ plane, and the HL-LHC could probe even smaller mixing values, including the corresponding benchmark points for both the maximal and PMNS-like patterns. Finally, we investigate complementarities with low-energy charged lepton flavor violation processes, where future searches can overlap with or exceed the LHC reach.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: Phys.Rev.D 114 (2026) 5, 055037",
  "arxiv": "2606.03809",
  "author_total": 2,
  "authors": [
    {
      "name": "Jin-Man Cai"
    },
    {
      "name": "Gang Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1hyy1gm6watf9.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2606.03809",
  "fulltext_url": null,
  "id": "1hyy1gm6watf9",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2606.03809",
  "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-ex"
    ],
    [
      "arxiv",
      "hep-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2606.03809",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1hyy1gm6watf9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:19Z",
      "native_id": "2606.03809",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2606.03809"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:hep-ex",
      "subfield": "high-energy"
    }
  ],
  "title": "Probing lepton flavor mixing in $W_R$ searches with machine learning at the LHC",
  "title_text": "Probing lepton flavor mixing in W_R searches with machine learning at the LHC",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:19Z",
  "url": "https://research.draconitic.com/paper/1hyy1gm6watf9",
  "venue": "arXiv",
  "versions": []
}