论文标题
使用PREATERENET在CEPC上使用PREATERELENET的$ R_B（R_C）$测量的喷气风味标签和测量的性能研究
Performance studies of jet flavor tagging and measurement of $R_b(R_c)$ using ParticleNet at CEPC
论文作者
论文摘要
喷气味标签在测量$ z $ boson的相对部分衰减宽度中起着至关重要的作用，称为$ r_b $（$ r_c $），这被认为是对新物理学的标准模型和敏感探针的基本测试。在这项研究中，采用了一种深度学习算法（特殊性）来增强喷气风味标记的性能。与循环电子colline Collider（CEPC）基线软件相比，$ c $ taging的效率和纯度提高了50 \％。为了通过这种新的风味标记方法测量$ r_b $（$ r_c $），我们采用了双重标记方法。 $ r_b $（$ r_c $）的精度显着提高，尤其是$ r_c $，$ r_c $的统计不确定性减少了40 \％。
Jet flavor tagging plays a crucial role in the measurement of relative partial decay widths of $Z$ boson, denoted as $R_b$($R_c$), which is considered as a fundamental test of the Standard Model and sensitive probe to new physics. In this study, a Deep Learning algorithm, ParticleNet, is employed to enhance the performance of jet flavor tagging. The combined efficiency and purity of $c$-tagging is improved by more than 50\% compared to the Circular Electron Positron Collider (CEPC) baseline software. In order to measure $R_b$($R_c$) with this new flavor tagging approach, we have adopted the double-tagging method. The precision of $R_b$($R_c$) is improved significantly, in particular to $R_c$, which has seen a reduction in statistical uncertainty by 40\%.
