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

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\%.

arXiv comment8 pages, 5 figures

References in corpus (13)

• PYTHIA 6.4 Physics and Manual
• PYTHIA 6.2 Physics and Manual
• WHIZARD: Simulating Multi-Particle Processes at LHC and ILC
• ParticleNet: Jet Tagging via Particle Clouds
• Update of the global electroweak fit and constraints on two-Higgs-doublet models
• Energy Flow Networks: Deep Sets for Particle Jets
• Reconstruction of physics objects at the Circular Electron Positron Collider with Arbor
• A Measurement of Rb using a Double Tagging Method
• Jet Flavour Tagging for Future Colliders with Fast Simulation
• Electroweak radiative corrections in Z boson decays
• Measurement of the branching ratios of the Z0 into heavy quarks
• Learning Physics at Future $e^{-}{e}^{+}$ Colliders with Machine
• Supersymmetry, Supergravity and R_b revisited in the light of LEP 2