An Efficient Lorentz Equivariant Graph Neural Network for Jet Tagging

Abstract

Deep learning methods have been increasingly adopted to study jets in particle physics. Since symmetry-preserving behavior has been shown to be an important factor for improving the performance of deep learning in many applications, Lorentz group equivariance - a fundamental spacetime symmetry for elementary particles - has recently been incorporated into a deep learning model for jet tagging. However, the design is computationally costly due to the analytic construction of high-order tensors. In this article, we introduce LorentzNet, a new symmetry-preserving deep learning model for jet tagging. The message passing of LorentzNet relies on an efficient Minkowski dot product attention. Experiments on two representative jet tagging benchmarks show that LorentzNet achieves the best tagging performance and improves significantly over existing state-of-the-art algorithms. The preservation of Lorentz symmetry also greatly improves the efficiency and generalization power of the model, allowing LorentzNet to reach highly competitive performance when trained on only a few thousand jets. Code and models are available at \url{https://github.com/sdogsq/LorentzNet-release}.

arXiv comment22 pages, 3 figures, and 7 tables

References in corpus (51)

• Decoupled Weight Decay Regularization
• Neural Message Passing for Quantum Chemistry
• ParticleNet: Jet Tagging via Particle Clouds
• Boosted objects: a probe of beyond the Standard Model physics
• Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning
• Jet Substructure at the Tevatron and LHC: New results, new tools, new benchmarks
• Jet Substructure at the Large Hadron Collider: Experimental Review
• Deep learning in color: towards automated quark/gluon jet discrimination
• Jet-Images -- Deep Learning Edition
• Deep-learning Top Taggers or The End of QCD?
• Jet Substructure Classification in High-Energy Physics with Deep Neural Networks
• Energy Flow Networks: Deep Sets for Particle Jets
• Jet Flavour Classification Using DeepJet
• Boosted objects and jet substructure at the LHC
• Equivariant flow-based sampling for lattice gauge theory
• Deep-learned Top Tagging with a Lorentz Layer
• Jet Flavor Classification in High-Energy Physics with Deep Neural Networks
• Jet-Images: Computer Vision Inspired Techniques for Jet Tagging
• Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
• Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks
• Energy flow polynomials: A complete linear basis for jet substructure
• Pulling Out All the Tops with Computer Vision and Deep Learning
• The Lund Jet Plane
• JEDI-net: a jet identification algorithm based on interaction networks
• QCD-Aware Recursive Neural Networks for Jet Physics
• Sampling using $SU(N)$ gauge equivariant flows
• ABCNet: An attention-based method for particle tagging
• Boosting $H\to b\bar{b}$ with Machine Learning
• How Much Information is in a Jet?
• Recursive Neural Networks in Quark/Gluon Tagging
• Novel Jet Observables from Machine Learning
• Lorentz Group Equivariant Neural Network for Particle Physics
• Jet Charge and Machine Learning
• Interaction networks for the identification of boosted $H\to b\overline{b}$ decays
• Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning
• Quark-Gluon Tagging: Machine Learning vs Detector
• Point Cloud Transformers applied to Collider Physics
• Casting a graph net to catch dark showers
• Interpretable Deep Learning for Two-Prong Jet Classification with Jet Spectra
• Equivariant Energy Flow Networks for Jet Tagging
• Lorentz Boost Networks: Autonomous Physics-Inspired Feature Engineering
• The Boosted Higgs Jet Reconstruction via Graph Neural Network
• Automating the Construction of Jet Observables with Machine Learning
• Quarks and gluons in the Lund plane
• Energy-weighted Message Passing: an infra-red and collinear safe graph neural network algorithm
• Infrared Safety of a Neural-Net Top Tagging Algorithm
• LieTransformer: Equivariant self-attention for Lie Groups
• Scalars are universal: Equivariant machine learning, structured like classical physics
• Neural Network-based Top Tagger with Two-Point Energy Correlations and Geometry of Soft Emissions
• Reconstructing boosted Higgs jets from event image segmentation
• Particle Convolution for High Energy Physics

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• Particle-level transformers for 95 GeV Higgs boson searches at future $e^{+}{e}^{-}$ Higgs factories
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• OmniLearned: A Foundation Model Framework for All Tasks Involving Jet Physics
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• Stable and Interpretable Jet Physics with IRC-Safe Equivariant Feature Extraction
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