mlsanityData to 14 Oct 2026

Jet-Images -- Deep Learning Edition

arXiv 1511.05190 ↗First posted 16 Nov 2015hep-ph physics.data-an stat.MLcited by 41

Not on the map. Why?

Abstract (arXiv)

Building on the notion of a particle physics detector as a camera and the collimated streams of high energy particles, or jets, it measures as an image, we investigate the potential of machine learning techniques based on deep learning architectures to identify highly boosted W bosons. Modern deep learning algorithms trained on jet images can out-perform standard physically-motivated feature driven approaches to jet tagging. We develop techniques for visualizing how these features are learned by the network and what additional information is used to improve performance. This interplay between physically-motivated feature driven tools and supervised learning algorithms is general and can be used to significantly increase the sensitivity to discover new particles and new forces, and gain a deeper understanding of the physics within jets.

Key facts. Cited by 41 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Nov 2015.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 10; 2022: 8; 2023: 6; 2024: 6; 2025: 7; 2026: 4.

41 papers.

Sort:

By area:

  1. 2022Particle Transformer for Jet Taggingcited by 98
  2. 2021A Living Review of Machine Learning for Particle Physicscited by 78
  3. 2021Machine Learning in the Search for New Fundamental Physicscited by 50
  4. 2021Particle Cloud Generation with Message Passing Generative Adversarial Networkscited by 46
  5. 2024Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physicscited by 38
  6. 2024A Lorentz-Equivariant Transformer for All of the LHCcited by 28
  7. 2023PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physicscited by 27
  8. 2021Hybrid Quantum-Classical Graph Convolutional Networkcited by 21
  9. 2021Topological Obstructions to Autoencodingcited by 20
  10. 2023EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusioncited by 17
  11. 2021A Cautionary Tale of Decorrelating Theory Uncertaintiescited by 11
  12. 2022Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learningcited by 10
29 more
  1. 2021Particle Convolution for High Energy Physicscited by 8
  2. 2022Leveraging universality of jet taggers through transfer learningcited by 5
  3. 2023Fast Neural Network Inference on FPGAs for Triggering on Long-Lived Particles at Colliderscited by 5
  4. 2023Equivariance Is Not All You Need: Characterizing the Utility of Equivariant Graph Neural Networks for Particle Physics Taskscited by 5
  5. 2024Optimal Equivariant Architectures from the Symmetries of Matrix-Element Likelihoodscited by 5
  6. 2021Shared Data and Algorithms for Deep Learning in Fundamental Physicscited by 4
  7. 2021Sequence-based Machine Learning Models in Jet Physicscited by 3
  8. 2022Feature Selection with Distance Correlationcited by 3
  9. 2023PCN: A Deep Learning Approach to Jet Tagging Utilizing Novel Graph Construction Methods and Chebyshev Graph Convolutionscited by 3
  10. 2026Explainable AI for Jet Tagging: A Comparative Study of GNNExplainer, GNNShap, and GradCAM for Jet Tagging in the Lund Jet Planecited by 3
  11. 2023Improving the performance of weak supervision searches using transfer and meta-learningcited by 2
  12. 2025Vision Transformers for End-to-End Quark-Gluon Jet Classification from Calorimeter Imagescited by 2
  13. 2025Stable and Interpretable Jet Physics with IRC-Safe Equivariant Feature Extractioncited by 2
  14. 2026Prompting Particle Physics: Tokenized Multi-modal Foundation Models for Combinatorially Many Taskscited by 2
  15. 2022New Metric Formulas that Include Measurement Errors in Machine Learning for Natural Sciencescited by 1
  16. 2022Decorrelation with conditional normalizing flowscited by 1
  17. 2022Learning Feynman Diagrams using Graph Neural Networkscited by 1
  18. 2025KIGNet: Physics-Motivated Multi-Graph Representation Learning for Explainable Jet Taggingcited by 1
  19. 2021Learning to increase matching efficiency in identifying additional b-jets in the tbartbbarb processcited by 0
  20. 2022Exploring the Universality of Hadronic Jet Classificationcited by 0
  21. 2024Particle Multi-Axis Transformer for Jet Taggingcited by 0
  22. 2024Lightweight Deep Learning Framework for Accurate Particle Flow Energy Reconstructioncited by 0
  23. 2024Scaling Particle Collision Data Analysiscited by 0
  24. 2025Learnable cut flow for high energy physicscited by 0
  25. 2025Jet Image Generation in High Energy Physics Using Diffusion Modelscited by 0
  26. 2025Jet Image Tagging Using Deep Learning: An Ensemble Modelcited by 0
  27. 2025SparsePixels: Efficient Convolution for Sparse Data on FPGAscited by 0
  28. 2026Comparison of Image Processing Models in Quark Gluon Jet Classificationcited by 0
  29. 2026Dissecting Jet-Tagger Through Mechanistic Interpretabilitycited by 0