ParticleNet and its application on CEPC Jet Flavor Tagging

TL;DR

This paper compares ParticleNet and LCFIPlus algorithms for quark flavor tagging at CEPC, demonstrating ParticleNet's superior performance and analyzing detector configuration impacts on tagging accuracy.

Contribution

It introduces ParticleNet's application to CEPC flavor tagging and evaluates its performance improvements over LCFIPlus.

Findings

ParticleNet improves flavor tagging accuracy by up to 75%.

Inner radius of vertex detector significantly affects tagging performance.

ParticleNet outperforms LCFIPlus in benchmark measurements.

Abstract

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.