Exploring an image-based $b$-jet tagging method using convolution neural networks

TL;DR

This paper investigates a novel image-based $b$-jet tagging method using convolutional neural networks, leveraging charged particle images around the primary vertex to improve flavor identification in high-energy physics.

Contribution

It introduces an innovative image-based approach for jet flavor tagging using CNNs, utilizing charged particle images, and demonstrates promising efficiency in $b$-jet identification.

Findings

Achieves 80-90% $b$-jet tagging efficiency in 20-100 GeV/$c$ range

Utilizes charged particle images from silicon tracking systems

Shows potential for improved jet flavor tagging accuracy

Abstract

Jet flavor tagging, the identification of jets originating from $c$-quarks, $b$-quarks, and other quarks (light quarks and gluons), is a crucial task in high-energy heavy-ion physics, as it enables the investigation of flavor-dependent responses within the hot and dense nuclear medium produced in heavy-ion collisions. Recently, several methods based on deep learning techniques, such as deep neural networks and graph neural networks, have been developed. These deep-learning-based methods demonstrate significantly improved performance compared to traditional methods that rely on track impact parameters and secondary vertices. In the tagging algorithms, various properties of jets and constituent charged particles are used as input parameters. We explore a new method based on images surrounding the primary vertex, utilizing charged particles within the jet cone, which can be measured using a silicon tracking system. For this initial experimental study, we assume the ideal performance of the tracking system. To analyze these images, we employed convolutional neural networks. The image-based flavor tagging method shows an 80-90% $b$-jet tagging efficiency for jets in the transverse momentum range from 20 to 100 GeV/$c$. This approach has the potential to significantly improve the accuracy of jet flavor tagging in high-energy nuclear physics experiments.