论文标题
深深地学到的希格斯dijet衰变在未来的Lepton Collider
Deeply Learned Preselection of Higgs Dijet Decays at Future Lepton Colliders
论文作者
论文摘要
未来的电子峰值山脉壁将在希格斯玻色子耦合的精确度量中发挥主要作用，这是粒子物理学中的核心利益之一。为了最大程度地提高性能，以测量Higgs耦合到底部，魅力和奇怪的夸克，我们开发了机器学习方法，以改善Higgs衰减到Dijets的事件选择。我们的方法基于增强的决策树（BDT），完全连接的神经网络（FCNN）和卷积神经网络（CNN）。我们发现基于BDT和FCNN的算法的表现优于常规剪切方法。随着我们使用FCNN改进了对Dijet事件的Higgs选择，用$ 16 \％$误差来测量Charm Quark信号强度，这大约比基于剪切的分析获得的$ 34 \％$精度要高两个因子。另外，奇怪的夸克信号强度被限制为$μ_{ss} \ Lessim 35 $，$ 95 \％$ c.l.使用FCNN，将其与$μ__{SS} \ Lessim 70 $进行比较。
Future electron-positron colliders will play a leading role in the precision measurement of Higgs boson couplings which is one of the central interests in particle physics. Aiming at maximizing the performance to measure the Higgs couplings to the bottom, charm and strange quarks, we develop machine learning methods to improve the selection of events with a Higgs decaying to dijets. Our methods are based on the Boosted Decision Tree (BDT), Fully-Connected Neural Network (FCNN) and Convolutional Neural Network (CNN). We find that the BDT and FCNN-based algorithms outperform the conventional cut-based method. With our improved selection of Higgs decaying to dijet events using the FCNN, the charm quark signal strength is measured with a $16\%$ error, which is roughly a factor of two better than the $34\%$ precision obtained by the cut-based analysis. Also, the strange quark signal strength is constrained as $μ_{ss} \lesssim 35$ at the $95\%$ C.L. with the FCNN, which is to be compared with $μ_{ss} \lesssim 70$ obtained by the cut-based method.
