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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.11232 |
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Table of Contents:
- 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 $τ$ 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 $\sqrt{s} = 14~\mathrm{TeV}$ with an integrated luminosity of $3000~\mathrm{fb}^{-1}$, the expected $2σ$ exclusion limits in the three- and four-lepton channels can reach vector-like lepton masses up to $620~\mathrm{GeV}$ and $490~\mathrm{GeV}$, respectively. These findings demonstrate that machine learning techniques can substantially improve the sensitivity of collider searches for vector-like leptons.