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}.
Dark matter and collider signals in an MSSM extension with vector-like multiplets
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Machine Learning Study on Single Production of a Singlet Vector-like Lepton at the Large Hadron Collider
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}.