Probing triple Higgs coupling with machine learning at the LHC

Abstract

Measuring the triple Higgs coupling is a crucial task in the LHC and future collider experiments. We apply the Message Passing Neural Network (MPNN) to the study of the non-resonant Higgs pair production process $pp\to hh$ in the final state with $2b+2\ell +E_{\mathrm{T}}^{\mathrm{miss}}$ 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 $2\ddot{I}ƒ$ upper bound (including a 10\% systematic uncertainty) on the production cross section of the Higgs pair is 3.7 times the predicted SM cross section at the LHC with the luminosity of 3000 fb${}^{-1}$, which will limit the triple Higgs coupling to the range of $[-3,11.5]$.

23 pages, discussions added, version accepted by PRD