Machine learning study on single production of a singlet vectorlike lepton at the Large Hadron Collider
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 $3000~\mathrm{fb}^{-1}$ under the assumption of negligible systematic uncertainties, the expected $2\sigma$ exclusion limits in the three- and four-lepton channels can reach vectorlike lepton masses up to $500$ and $405~\mathrm{GeV}$ 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.