  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}.
