mlsanityData to 14 Oct 2026

Point Cloud Transformers applied to Collider Physics

arXiv 2102.05073 ↗First posted 9 Feb 2021physics.data-an cs.LG hep-excited by 23

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Abstract (arXiv)

Methods for processing point cloud information have seen a great success in collider physics applications. One recent breakthrough in machine learning is the usage of Transformer networks to learn semantic relationships between sequences in language processing. In this work, we apply a modified Transformer network called Point Cloud Transformer as a method to incorporate the advantages of the Transformer architecture to an unordered set of particles resulting from collision events. To compare the performance with other strategies, we study jet-tagging applications for highly-boosted particles.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). Builds on 15 of its 33 references that are in the map. First posted on arXiv 9 Feb 2021.

Builds on

15 of 33 references.

References · 15

  1. 2017Attention Is All You Needcited by 60,361
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2017Graph Attention Networkscited by 8,923
  5. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,367
  6. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
  7. 2020PCT: Point cloud transformercited by 732
  8. 2020Visual Transformers: Token-based Image Representation and Processing for Computer Visioncited by 334
  9. 2018Fast inference of deep neural networks in FPGAs for particle physicscited by 133
  10. 2018Energy Flow Networks: Deep Sets for Particle Jetscited by 84
  11. 2019ParticleNet: Jet Tagging via Particle Cloudscited by 64
  12. 2019Learning representations of irregular particle-detector geometry with distance-weighted graph networkscited by 43
3 more
  1. 2020Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networkscited by 22
  2. 2016Deep learning in color: towards automated quark/gluon jet discriminationcited by 21
  3. 2019GAPNet: Graph Attention based Point Neural Network for Exploiting Local Feature of Point Cloudcited by 15

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 5; 2023: 5; 2024: 5; 2025: 6; 2026: 2.

23 papers.

Sort:

  1. 2022Particle Transformer for Jet Taggingcited by 98
  2. 2024Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Modelscited by 46
  3. 2023PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physicscited by 27
  4. 2023Explainable Equivariant Neural Networks for Particle Physics: PELICANcited by 24
  5. 2022Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learningcited by 10
  6. 2023Polynomial Width is Sufficient for Set Representation with High-dimensional Featurescited by 6
  7. 2023A Comparison Between Invariant and Equivariant Classical and Quantum Graph Neural Networkscited by 6
  8. 2024Ultra Fast Transformers on FPGAs for Particle Physics Experimentscited by 6
  9. 2024Learning the Simplicity of Scattering Amplitudescited by 6
  10. 2024TrackFormers: In Search of Transformer-Based Particle Tracking for the High-Luminosity LHC Eracited by 4
  11. 2022Feature Selection with Distance Correlationcited by 3
  12. 2023PCN: A Deep Learning Approach to Jet Tagging Utilizing Novel Graph Construction Methods and Chebyshev Graph Convolutionscited by 3
11 more
  1. 2025Vision Transformers for End-to-End Quark-Gluon Jet Classification from Calorimeter Imagescited by 2
  2. 2025Stable and Interpretable Jet Physics with IRC-Safe Equivariant Feature Extractioncited by 2
  3. 2026Prompting Particle Physics: Tokenized Multi-modal Foundation Models for Combinatorially Many Taskscited by 2
  4. 2024Image and Point-cloud Classification for Jet Analysis in High-Energy Physics: A surveycited by 1
  5. 2025Generating particle physics Lagrangians with transformerscited by 1
  6. 2025BitHEP -- The Limits of Low-Precision ML in HEPcited by 1
  7. 2025KIGNet: Physics-Motivated Multi-Graph Representation Learning for Explainable Jet Taggingcited by 1
  8. 2025From Small to Large: Generalization Bounds for Transformers on Variable-Size Inputscited by 1
  9. 2022Deep learning techniques for energy clustering in the CMS ECALcited by 0
  10. 2022POViT: Vision Transformer for Multi-objective Design and Characterization of Nanophotonic Devicescited by 0
  11. 2026Transfer Learning Across Fast- and Full-Simulation Domains in High-Energy Physicscited by 0