未来轻子对撞机上Higgs双喷衰变的预选研究
Deeply Learned Preselection of Higgs Dijet Decays at Future Lepton Colliders
摘要：
希格斯玻色子玻色子耦合是粒子物理学的核心研究课题之一，未来的正负电子对撞机将在这一领域发挥重要作用。为了最大限度地提高测量希格斯耦合到底部，魅力和奇怪的夸克的性能，我们开发了机器学习方法，以改善希格斯衰变到dijets事件的选择。我们的方法是基于提升决策树（BDT），全连接神经网络（FCNN）和卷积神经网络（CNN）。我们发现，BDT和FCNN为基础的算法优于传统的切割为基础的方法。我们改进了使用FCNN对希格斯粒子衰变为双喷流事件的选择，测量魅夸克信号强度的误差为$16\%$，这比基于切割的分析获得的$34\%$精度大约高出两倍。此外，奇夸克信号强度在$95\%$ C. L处被约束为$μ_{ss} \lesssim 35$。与FCNN，这是比较与$μ_{ss} \lesssim 70$通过基于切割的方法获得。
英文摘要：
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.
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。
