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Jet flavor tagging plays a crucial role in the measurement of relativepartial decay widths of $Z$ boson, denoted as $R_b$($R_c$), which is consideredas a fundamental test of the Standard Model and sensitive probe to new physics.In this study, a Deep Learning algorithm, ParticleNet, is employed to enhancethe performance of jet flavor tagging. The combined efficiency and purity of$c$-tagging is improved by more than 50\% compared to the Circular ElectronPositron Collider (CEPC) baseline software. In order to measure $R_b$($R_c$)with this new flavor tagging approach, we have adopted the double-taggingmethod. The precision of $R_b$($R_c$) is improved significantly, in particularto $R_c$, which has seen a reduction in statistical uncertainty by 40\%.

Performance studies of jet flavor tagging and measurement of $R_b(R_c)$ using ParticleNet at CEPC