Machine Learning Study on Single Production of a Singlet Vector-like Lepton at the Large Hadron Collider

Vector-like leptons are non-chiral, 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 vector-like 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 $\sqrt{s}=14\mathrm{T}\mathrm{e}\mathrm{V}$ with an integrated luminosity of $3000{\mathrm{f}\mathrm{b}}^{-1}$, the expected $2\sigma$ exclusion limits in the three- and four-lepton channels can reach vector-like lepton masses up to $620\mathrm{G}\mathrm{e}\mathrm{V}$ and $490\mathrm{G}\mathrm{e}\mathrm{V}$, respectively. These findings demonstrate that machine learning techniques can substantially improve the sensitivity of collider searches for vector-like leptons.