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2022

An Efficient Lorentz Equivariant Graph Neural Network for Jet Tagging

Gong, Shiqi, Meng, Qi, Zhang, Jue et al.

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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.

An Efficient Lorentz Equivariant Graph Neural Network for Jet Tagging · Around

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