Sensitivity of two-Higgs-doublet models on Higgs-pair production via bb over line bb over line final state

Higgs boson pair production is a well-known probe of the structure of the electroweak symmetry breaking sector. We illustrate this using the gluon-fusion processes pp -H -hh -(bb over bar )(bb over bar ) in the framework of two-Higgs-doublet models and show how a machine learning approach (three-stream convolutional neural network) can substantially improve the signal-background discrimination and thus improve the sensitivity coverage of the relevant parameter space. We further show that such gg -hh -bb over bar bb over bar processes can probe the parameter space currently allowed by HIGGSSIGNALS and HIGGSBOUNDS at the HL-LHC. Results are presented for 2HDM types I through IV.

Automated summary

This paper investigates the production of Higgs boson pairs in the electroweak symmetry breaking sector. The machine learning approach used in this study significantly improves signal-background discrimination and enhances the sensitivity coverage of the parameter space. The results demonstrate that the processes of gg-hh-bb\¯bb¯ can probe the parameter space allowed by HIGGSSIGNALS and HIGGSBOUNDS at the HL-LHC.