XGBoost multivariate analysis extends the 5-sigma discovery reach for singly produced vector-like bottom quarks decaying via heavy neutral Higgs bosons to 1.6 TeV at the HL-LHC with 3 ab^{-1}.
Hayrapetyanet al.(CMS), Phys
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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}.
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Probing Heavy Neutral Higgs Bosons via Single Vector-Like Bottom Quark Production at the HL-LHC
XGBoost multivariate analysis extends the 5-sigma discovery reach for singly produced vector-like bottom quarks decaying via heavy neutral Higgs bosons to 1.6 TeV at the HL-LHC with 3 ab^{-1}.
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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}.