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→H→hh→(bbˉ)(bbˉ) 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→hh→bbˉbbˉ 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.
@article{arxiv.2207.09602,
title = {Sensitivity on Two-Higgs-Doublet Models from Higgs-Pair Production via $b\bar{b}b\bar{b}$ Final State},
author = {Kingman Cheung and Yi-Lun Chung and Shih-Chieh Hsu},
journal= {arXiv preprint arXiv:2207.09602},
year = {2022}
}