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ParticleNet: Jet Tagging via Particle Clouds

arXiv 1902.08570 ↗First posted 22 Feb 2019hep-ph cs.CV hep-excited by 64

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Abstract (arXiv)

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.

Key facts. Cited by 64 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Feb 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 12; 2022: 9; 2023: 7; 2024: 8; 2025: 11; 2026: 17.

64 papers.

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  1. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,131
  2. 2022Particle Transformer for Jet Taggingcited by 95
  3. 2021A Living Review of Machine Learning for Particle Physicscited by 77
  4. 2021Machine Learning in the Search for New Fundamental Physicscited by 50
  5. 2021Particle Cloud Generation with Message Passing Generative Adversarial Networkscited by 45
  6. 2021MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networkscited by 36
  7. 2022Evaluating generative models in high energy physicscited by 31
  8. 2024A Lorentz-Equivariant Transformer for All of the LHCcited by 27
  9. 2021Point Cloud Transformers applied to Collider Physicscited by 23
  10. 2021Graph Neural Networks for Charged Particle Tracking on FPGAscited by 17
  11. 2022Lorentz group equivariant autoencoderscited by 15
  12. 2021A FAIR and AI-ready Higgs boson decay datasetcited by 14
38 more
  1. 2022Machine Learning for Particle Flow Reconstruction at CMScited by 14
  2. 2021Explaining machine-learned particle-flow reconstructioncited by 12
  3. 2024Ultrafast jet classification on FPGAs for the HL-LHCcited by 12
  4. 2021Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distancecited by 10
  5. 2022Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learningcited by 10
  6. 2023Anomalies, Representations, and Self-Supervisioncited by 10
  7. 2021Particle Convolution for High Energy Physicscited by 8
  8. 2023Topological Reconstruction of Particle Physics Processes using Graph Neural Networkscited by 8
  9. 2026EveNet: A Foundation Model for Particle Collision Data Analysiscited by 8
  10. 2022Leveraging universality of jet taggers through transfer learningcited by 5
  11. 2024Optimal Equivariant Architectures from the Symmetries of Matrix-Element Likelihoodscited by 5
  12. 2021Shared Data and Algorithms for Deep Learning in Fundamental Physicscited by 4
  13. 2022Invariant Representation Driven Neural Classifier for Anti-QCD Jet Taggingcited by 4
  14. 2022Do graph neural networks learn traditional jet substructure?cited by 4
  15. 2022FAIR AI Models in High Energy Physicscited by 4
  16. 2023PCN: A Deep Learning Approach to Jet Tagging Utilizing Novel Graph Construction Methods and Chebyshev Graph Convolutionscited by 3
  17. 2025SURFing to the Fundamental Limit of Jet Taggingcited by 3
  18. 2026Explainable AI for Jet Tagging: A Comparative Study of GNNExplainer, GNNShap, and GradCAM for Jet Tagging in the Lund Jet Planecited by 3
  19. 2023Versatile Energy-Based Probabilistic Models for High Energy Physicscited by 2
  20. 2023AdamMCMC: Combining Metropolis Adjusted Langevin with Momentum-based Optimizationcited by 2
  21. 2025Stable and Interpretable Jet Physics with IRC-Safe Equivariant Feature Extractioncited by 2
  22. 2026Towards Tensor Network Models for Low-Latency Jet Tagging on FPGAscited by 2
  23. 2026Statistical Properties of Training & Generalizationcited by 2
  24. 2023JetLOV: Enhancing Jet Tree Tagging through Neural Network Learning of Optimal LundNet Variablescited by 1
  25. 2023SAIBench: A Structural Interpretation of AI for Science Through Benchmarkscited by 1
  26. 2024A multicategory jet image classification framework using deep neural networkcited by 1
  27. 2024Novel machine learning applications at the LHCcited by 1
  28. 2024Reconstruction of boosted and resolved multi-Higgs-boson events with symmetry-preserving attention networkscited by 1
  29. 2025BitHEP -- The Limits of Low-Precision ML in HEPcited by 1
  30. 2025KIGNet: Physics-Motivated Multi-Graph Representation Learning for Explainable Jet Taggingcited by 1
  31. 2026jBOT: Semantic Jet Representation Clustering Emerges from Self-Distillationcited by 1
  32. 2026From Information Geometry to Jet Substructure: A Triality of Cumulant Tensors, Energy Correlators, and Hypergraphscited by 1
  33. 2026Machine-learned particle flow as a foundation model for collider physicscited by 1
  34. 2026Prompting Particle Physics: Tokenized Multi-modal Foundation Models for Combinatorially Many Taskscited by 1
  35. 2024Moments of Clarity: Streamlining Latent Spaces in Machine Learning using Moment Poolingcited by 0
  36. 2024Scaling Particle Collision Data Analysiscited by 0
  37. 2025Learnable cut flow for high energy physicscited by 0
  38. 2025Tagging fully hadronic exotic decays of the vectorlike B quark using a graph neural networkcited by 0

50 most cited of 64 All 64 (JSON)