Probing the triple Higgs boson coupling with machine learning at the LHC
Murat Abdughani, Daohan Wang, Lei Wu, Jin Min Yang, Jun Zhao
Abstract
Measuring the triple Higgs boson coupling is a crucial task in the LHC and future collider experiments. We apply the message passing neural network (MPNN) to the study of nonresonant Higgs pair production process $pp\ensuremath{\rightarrow}hh$ in the final state with $2b+2\ensuremath{\ell}+{E}_{\mathrm{T}}^{\mathrm{miss}}$ at the LHC. Although the MPNN can improve the signal significance, it is still challenging to observe such a process at the LHC. We find that a $2\ensuremath{\sigma}$ upper bound (including a 10% systematic uncertainty) on the production cross section of the Higgs pair is 3.7 times the predicted Standard Model cross section at the LHC with the luminosity of $3000\text{ }\text{ }{\mathrm{fb}}^{\ensuremath{-}1}$, which will limit the triple Higgs boson coupling to the range of $[\ensuremath{-}3,11.5]$.