arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2005.11086hep-phhep-ex
收藏

Probing triple Higgs coupling with machine learning at the LHC

Murat Abdughani, Daohan Wang, Lei Wu, Jin Min Yang, Jun Zhao

英文摘要:

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 $2b + 2\ell + E_{\rm T}^{\rm miss}$ 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 $2σ$ 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 $[-3,11.5]$.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑