Papers › ParticleNet: Jet Tagging via Particle Clouds
ParticleNet: Jet Tagging via Particle Clouds
Huilin Qu, Loukas Gouskos
How to represent a jet is at the core of machine learning on jet physics. Inspired by the notion of point clouds, we propose a new approach that considers a jet as an unordered set of its constituent particles, effectively a "particle cloud". Such a particle cloud representation of jets is efficient in incorporating raw information of jets and also explicitly respects the permutation symmetry. Based on the particle cloud representation, we propose ParticleNet, a customized neural network architecture using Dynamic Graph Convolutional Neural Network for jet tagging problems. The ParticleNet architecture achieves state-of-the-art performance on two representative jet tagging benchmarks and is improved significantly over existing methods.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Jet Tagging | JetClass | ParticleNet | #Params | 370000 | #2 of 2 | Archive leaderboard | report |
| Jet Tagging | JetClass | ParticleNet | AUC | 0.9849 | #2 of 2 | Archive leaderboard | report |
| Jet Tagging | JetClass | ParticleNet | Accuracy | 0.844 | #2 of 2 | Archive leaderboard | report |
| Jet Tagging | JetClass | ParticleNet | FLOPs | 540000000 | #2 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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