ParticleNet and its application on CEPC Jet Flavor Tagging

Yongfeng Zhu, Hao Liang, Yuexin Wang, Huilin Qu, Chen Zhou, Manqi Ruan
High Energy Physics - Experiment, High Energy Physics - Experiment (hep-ex)
2023-09-22 16:00:00
Identification of quark flavor is essential for collider experiments in high-energy physics, relying on the flavor tagging algorithm. In this study, using a full simulation of the Circular Electron Positron Collider (CEPC), we investigated the flavor tagging performance of two different algorithms: ParticleNet, originally developed at CMS, and LCFIPlus, the current flavor tagging algorithm employed at CEPC. Compared to LCFIPlus, ParticleNet significantly enhances flavor tagging performance, resulting in a significant improvement in benchmark measurement accuracy, i.e., a 36% improvement for $\nu\bar{\nu}H\to c\bar{c}$ measurement and a 75% improvement for $|V_{cb}|$ measurement via W boson decay when CEPC operates as a Higgs factory at the center-of-mass energy of 240 GeV and integrated luminosity of 5.6 $ab^{-1}$. We compared the performance of ParticleNet and LCFIPlus at different vertex detector configurations, observing that the inner radius is the most sensitive parameter, followed by material budget and spatial resolution.
PDF: ParticleNet and its application on CEPC Jet Flavor Tagging.pdf
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