Machine learning approach to Higgs Pair Production

• Prof.Kingman Cheung (Nat'l Tsing Hua Univ. / Konkuk Univ.)

Room: # 326 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)pp→H→hh→(b bˉ )(b bˉ ) 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 gg \to hh \to b \bar b b\bar bgg→hh→bbˉ bbˉ 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.