Probing the triple Higgs boson coupling with machine learning at the LHC

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 $[\ensuremath{-}3,11.5]$.