摘要
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¯)(bb¯) 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¯bb¯ 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.