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

TL;DR

This paper demonstrates that using ParticleNet deep learning significantly improves jet flavor tagging performance at CEPC, leading to more precise measurements of the $R_b$ and $R_c$ parameters of the Z boson, crucial for testing the Standard Model.

Contribution

The study introduces ParticleNet for jet flavor tagging at CEPC, achieving over 50% efficiency and purity improvements, and applies a double-tagging method to enhance $R_b$ and $R_c$ measurement precision.

Findings

$c$-tagging efficiency and purity increased by over 50%.

Statistical uncertainty in $R_c$ measurement reduced by 40%.

Enhanced flavor tagging improves fundamental tests of the Standard Model.

Abstract

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\%.