论文标题
在LHC上探测三重希格斯与机器学习的耦合
Probing triple Higgs coupling with machine learning at the LHC
论文作者
论文摘要
在LHC和未来的对撞机实验中，测量三重希格斯耦合是至关重要的任务。我们将消息传递神经网络（MPNN）应用于对最终状态的非共鸣Higgs对生产过程$ pp \ to hh $的研究，其$ 2B + 2 \ ell + e _ {\ rm t}^{\ rm t}^{\ rm miss} $在LHC处于LHC。尽管MPNN可以提高信号的意义，但在LHC上观察这种过程仍然具有挑战性。我们发现，Higgs对的生产横截面上的$2σ$上限（包括10 \％的系统不确定性）是LHC预测的SM横截面的3.7倍，其发光度为3000 fb $^{ - 1} $，这将限制三重higgs coupling to $ [ - 3,3,11.11.5] $]。
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 $[-3,11.5]$.
