  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.
