Machine learning study on single production of a singlet vectorlike lepton at the Large Hadron Collider

Le résumé fourni par la source

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 τ 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 s = 14 TeV with an integrated luminosity of 3000 fb − 1 under the assumption of negligible systematic uncertainties, the expected 2 σ exclusion limits in the three- and four-lepton channels can reach vectorlike lepton masses up to 500 and 405 GeV in the parameter region allowed by the electroweak oblique parameter constraint, respectively. These findings demonstrate that machine learning techniques can substantially improve the sensitivity of collider searches for vectorlike leptons.

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