Machine-learning discovery potential for a heavy Higgs boson in e⁺e⁻→ ZH at a future lepton collider

We study the discovery potential for a CP-even heavy neutral Higgs boson H in the Type-I two-Higgs-doublet model (2HDM) at a future e⁺e⁻ collider with √s=500~GeV, focusing on the process e⁺e⁻→ ZH with H→ WW, and with Z→ or Z→νν. The final states are four jets plus two leptons (4j2) and four jets plus missing transverse momentum ($4j2ν$). We compare a cut-based analysis with two multivariate strategies: a one-dimensional Boosted Decision Tree (BDT) trained per mass point, and a two-dimensional (BDT × mH) template analysed with a profile likelihood. The 2D template improves the expected discovery significance by a factor of $2.5$--$3.6$ in the 4j2 channel and by a factor of $5$--$18$ in the $4j2ν$ channel relative to the cut-based analysis, with the largest gains at high mH where the cut-based mass window loses efficiency. We translate the expected exclusions into $95%$ CL upper limits on σ× BR and discuss the implications for the Type-I 2HDM parameter space.