Performance studies of jet flavor tagging and measurement of Rb(Rc) using ParticleNet at CEPC

Abstract

Jet flavor tagging plays a crucial role in the measurement of relative partial decay widths of [Formula: see text] boson, denoted as [Formula: see text]([Formula: see text]), 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 [Formula: see text]-tagging is improved by more than 50% compared to the Circular Electron Positron Collider (CEPC) baseline software. In order to measure [Formula: see text]([Formula: see text]) with this new flavor tagging approach, we have adopted the double-tagging method. The precision of [Formula: see text]([Formula: see text]) is improved significantly, in particular to [Formula: see text], which has seen a reduction in statistical uncertainty by 40%.