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Jet tagging is a classification problem in high-energy physics experimentsthat aims to identify the collimated sprays of subatomic particles, jets, fromparticle collisions and tag them to their emitter particle. Advances in jettagging present opportunities for searches of new physics beyond the StandardModel. Current approaches use deep learning to uncover hidden patterns incomplex collision data. However, the representation of jets as inputs to a deeplearning model have been varied, and often, informative features are withheldfrom models. In this study, we propose a graph-based representation of a jetthat encodes the most information possible. To learn best from thisrepresentation, we design Particle Chebyshev Network (PCN), a graph neuralnetwork (GNN) using Chebyshev graph convolutions (ChebConv). ChebConv has beendemonstrated as an effective alternative to classical graph convolutions inGNNs and has yet to be explored in jet tagging. PCN achieves a substantialimprovement in accuracy over existing taggers and opens the door to futurestudies into graph-based representations of jets and ChebConv layers inhigh-energy physics experiments. Code is available athttps://github.com/YVSemlani/PCN-Jet-Tagging.

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