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 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 ${\mu }_{ss}\lesssim 35$ at the 95% C.L. with the FCNN, which is to be compared with ${\mu }_{ss}\lesssim 70$ obtained by the cut-based method.

未来的正负电子对撞机将在希格斯玻色子耦合的精密测量中发挥主导作用，这是粒子物理学的核心兴趣之一。为了最大限度地提高测量希格斯耦合到底部、粲夸克和奇夸克的性能，我们开发了机器学习方法来改进对希格斯衰变为迪杰特的事件的选择。我们的方法基于增强决策树 (BDT)、全连接神经网络 (FCNN) 和卷积神经网络 (CNN)。我们发现 BDT 和 FCNN 算法优于传统的基于切割的方法。随着我们使用 FCNN 改进了对 Higgs 衰减到 dijet 事件的选择，粲夸克信号强度的测量误差为 16%，这比基于切割的分析获得的 34% 精度大约高出两倍。还，${\mu }_{ss}\lesssim 35$在 95% CL 与 FCNN 进行比较${\mu }_{ss}\lesssim 70$通过基于切割的方法获得。