{"paper":{"title":"Machine learning study on single production of a singlet vectorlike lepton at the Large Hadron Collider","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Machine learning with XGBoost extends LHC exclusion limits for singlet vector-like leptons to 620 GeV.","cross_cats":[],"primary_cat":"hep-ph","authors_text":"Hong-Hao Zhang, Shiyu Wang, Yiheng Cui, Zhao-Huan Yu","submitted_at":"2026-04-13T09:36:42Z","abstract_excerpt":"Vectorlike leptons are nonchiral, colorless fermions from new physics beyond the Standard Model, appearing in many theoretical extensions. We investigate the prospect for detecting the single production of a singlet vectorlike lepton that mixes with the $\\tau$ lepton at the Large Hadron Collider. The corresponding final states are classified as the three- and four-lepton search channels. The machine learning algorithm XGBoost is employed to enhance signal-background discrimination. Our analysis indicates that, at $\\sqrt{s} = 14~\\mathrm{TeV}$ with an integrated luminosity of $3000~\\mathrm{fb}^{"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Our analysis indicates that, at √s = 14 TeV with an integrated luminosity of 3000 fb^{-1}, the expected 2σ exclusion limits in the three- and four-lepton channels can reach vector-like lepton masses up to 620 GeV and 490 GeV, respectively.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The projected limits assume that Monte Carlo simulations accurately capture both signal kinematics (dependent on the tau-mixing parameter) and all relevant backgrounds, and that no unaccounted systematic uncertainties degrade the XGBoost performance; if mixing is smaller than assumed or backgrounds are mismodeled, the reach would be reduced.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"XGBoost machine learning improves discrimination in LHC searches for singlet vector-like leptons, yielding projected 2σ mass exclusion limits of 620 GeV (three-lepton) and 490 GeV (four-lepton) at 14 TeV with 3000 fb^{-1}.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Machine learning with XGBoost extends LHC exclusion limits for singlet vector-like leptons to 620 GeV.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"4798fa74bbc1868db93b94eda10bcc9949aacd1c9d7b85cae07d760b9229cbf7"},"source":{"id":"2604.11232","kind":"arxiv","version":2},"verdict":{"id":"84bd7596-e1d0-4e4a-b149-46e973ae841d","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T15:11:53.081226Z","strongest_claim":"Our analysis indicates that, at √s = 14 TeV with an integrated luminosity of 3000 fb^{-1}, the expected 2σ exclusion limits in the three- and four-lepton channels can reach vector-like lepton masses up to 620 GeV and 490 GeV, respectively.","one_line_summary":"XGBoost machine learning improves discrimination in LHC searches for singlet vector-like leptons, yielding projected 2σ mass exclusion limits of 620 GeV (three-lepton) and 490 GeV (four-lepton) at 14 TeV with 3000 fb^{-1}.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The projected limits assume that Monte Carlo simulations accurately capture both signal kinematics (dependent on the tau-mixing parameter) and all relevant backgrounds, and that no unaccounted systematic uncertainties degrade the XGBoost performance; if mixing is smaller than assumed or backgrounds are mismodeled, the reach would be reduced.","pith_extraction_headline":"Machine learning with XGBoost extends LHC exclusion limits for singlet vector-like leptons to 620 GeV."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.11232/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}