Jet Flavor Classification in High-Energy Physics with Deep Neural Networks

TL;DR

This paper explores deep neural networks for jet flavor classification in high-energy physics, demonstrating that using detailed track and vertex data improves classification accuracy over traditional methods.

Contribution

It introduces deep learning approaches that utilize raw tracking data, surpassing existing expert-engineered feature-based classifiers in jet flavor tagging.

Findings

Deep networks can match or outperform state-of-the-art classifiers.

Using detailed track and vertex information enhances classification performance.

Lower-level tracking data remains challenging but beneficial when combined with other features.

Abstract

Classification of jets as originating from light-flavor or heavy-flavor quarks is an important task for inferring the nature of particles produced in high-energy collisions. The large and variable dimensionality of the data provided by the tracking detectors makes this task difficult. The current state-of-the-art tools require expert data-reduction to convert the data into a fixed low-dimensional form that can be effectively managed by shallow classifiers. We study the application of deep networks to this task, attempting classification at several levels of data, starting from a raw list of tracks. We find that the highest-level lowest-dimensionality expert information sacrifices information needed for classification, that the performance of current state-of-the-art taggers can be matched or slightly exceeded by deep-network-based taggers using only track and vertex information, that classification using only lowest-level highest-dimensionality tracking information remains a difficult task for deep networks, and that adding lower-level track and vertex information to the classifiers provides a significant boost in performance compared to the state-of-the-art.