摘要
Higgs boson pair production is well known to probe the structure of the electroweak symmetry breaking sector. We illustrate using the gluon-fusion processpp → H → h h → (bb̄) (bb̄)in the framework of two-Higgs-doublet models and how the machine learning approach (three-stream convolutional neural network) can substantially improve the signal-background discrimination and thus improves the sensitivity coverage of the relevant parameter space. We show that suchgg → hh → b b̄ bb̄process can further probe the currently allowed parameter space by HiggsSignals and HiggsBounds at the HL-LHC. The results for Types I to IV are shown.