Title : Machine learning approach to Higgs Pair Production
Speaker : Prof. Kingman Cheung (NTHU, Taiwan)
Abstract : Higgs boson pair production is well known to probe the structure of the electroweak symmetry breaking sector. We illustrate using the gluon-fusion process $pp \to H \to h h \to (b\bar b) (b\bar b)$ 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 such gluon fusion process 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.