mlsanityData to 7 Oct 2026

Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networks

arXiv 2010.09206 ↗First posted 19 Oct 2020hep-ex cs.LG hep-phcited by 22

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Abstract (arXiv)

Top quarks, produced in large numbers at the Large Hadron Collider, have a complex detector signature and require special reconstruction techniques. The most common decay mode, the "all-jet" channel, results in a 6-jet final state which is particularly difficult to reconstruct in pp collisions due to the large number of permutations possible. We present a novel approach to this class of problem, based on neural networks using a generalized attention mechanism, that we call Symmetry Preserving Attention Networks (SPA-Net). We train one such network to identify the decay products of each top quark unambiguously and without combinatorial explosion as an example of the power of this technique.This approach significantly outperforms existing state-of-the-art methods, correctly assigning all jets in 93.0% of 6-jet, 87.8% of 7-jet, and 82.6% of ≥ 8-jet events respectively.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Oct 2020.

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Citing papers per year: 2021: 3; 2022: 4; 2023: 7; 2024: 5; 2025: 0; 2026: 3.

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  1. 2021Scalars are universal: Equivariant machine learning, structured like classical physicscited by 127
  2. 2024Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Modelscited by 44
  3. 2023The Exact Sample Complexity Gain from Invariances for Kernel Regressioncited by 27
  4. 2021Point Cloud Transformers applied to Collider Physicscited by 23
  5. 2023Deep Learning Symmetries and Their Lie Groups, Algebras, and Subalgebras from First Principlescited by 22
  6. 2023End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physicscited by 22
  7. 2021SPANet: Generalized Permutationless Set Assignment for Particle Physics using Symmetry Preserving Attentioncited by 18
  8. 2022Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challengescited by 12
  9. 2022nu-Flows: Conditional Neutrino Regressioncited by 11
  10. 2022Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learningcited by 10
  11. 2023nu2-Flows: Fast and improved neutrino reconstruction in multi-neutrino final states with conditional normalizing flowscited by 10
  12. 2023Topological Reconstruction of Particle Physics Processes using Graph Neural Networkscited by 8
10 more
  1. 2022The Quarks of Attentioncited by 7
  2. 2023Reconstruction of Unstable Heavy Particles Using Deep Symmetry-Preserving Attention Networkscited by 7
  3. 2023Oracle-Preserving Latent Flowscited by 5
  4. 2024Optimal Equivariant Architectures from the Symmetries of Matrix-Element Likelihoodscited by 5
  5. 2024Reconstructing short-lived particles using hypergraph representation learningcited by 4
  6. 2024PASCL: Supervised Contrastive Learning with Perturbative Augmentation for Particle Decay Reconstructioncited by 3
  7. 2024Reconstruction of boosted and resolved multi-Higgs-boson events with symmetry-preserving attention networkscited by 1
  8. 2026Unknown Unknowns: Model Misspecification in Machine Learning for Physicscited by 1
  9. 2026Pairton: Iterative Reconstruction of Short-Lived Particlescited by 1
  10. 2026Comprehensive reconstruction of collider events with hypergraph representation learning and graph-conditioned diffusioncited by 0