PCN: A Deep Learning Approach to Jet Tagging Utilizing Novel Graph Construction Methods and Chebyshev Graph Convolutions

TL;DR

This paper introduces PCN, a novel graph neural network utilizing Chebyshev graph convolutions for jet tagging in high-energy physics, significantly improving accuracy by leveraging a comprehensive graph-based jet representation.

Contribution

The study proposes a new graph-based jet representation and a GNN architecture with ChebConv layers, demonstrating improved performance over existing methods in jet tagging.

Findings

PCN outperforms existing jet taggers in accuracy.

Graph-based representation encodes more information from jets.

Chebyshev graph convolutions are effective for high-energy physics data.

Abstract

Jet tagging is a classification problem in high-energy physics experiments that aims to identify the collimated sprays of subatomic particles, jets, from particle collisions and tag them to their emitter particle. Advances in jet tagging present opportunities for searches of new physics beyond the Standard Model. Current approaches use deep learning to uncover hidden patterns in complex collision data. However, the representation of jets as inputs to a deep learning model have been varied, and often, informative features are withheld from models. In this study, we propose a graph-based representation of a jet that encodes the most information possible. To learn best from this representation, we design Particle Chebyshev Network (PCN), a graph neural network (GNN) using Chebyshev graph convolutions (ChebConv). ChebConv has been demonstrated as an effective alternative to classical graph convolutions in GNNs and has yet to be explored in jet tagging. PCN achieves a substantial improvement in accuracy over existing taggers and opens the door to future studies into graph-based representations of jets and ChebConv layers in high-energy physics experiments. Code is available at https://github.com/YVSemlani/PCN-Jet-Tagging.