An efficient Lorentz equivariant graph neural network for jet tagging

doi:10.1007/JHEP07(2022)030

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	An efficient Lorentz equivariant graph neural network for jet tagging
	Gong,Shiqi 1,5; Meng,Qi 2; Zhang,Jue 2; Qu,Huilin 3; Li,Congqiao 4; Qian,Sitian 4; Du,Weitao 1; Ma,Zhi-Ming 1; Liu,Tie-Yan 2
	2022-07-05
Source Publication	Journal of High Energy Physics
Volume	2022 Issue:7
Abstract	AbstractDeep 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.
Keyword	Jets and Jet Substructure Top Quark
DOI	10.1007/JHEP07(2022)030
Language	英语
WOS ID	BMC:10.1007/JHEP07(2022)030
Publisher	Springer Berlin Heidelberg
Citation statistics
Document Type	期刊论文
Identifier	http://ir.amss.ac.cn/handle/2S8OKBNM/60421
Collection	中国科学院数学与系统科学研究院
Corresponding Author	Meng,Qi
Affiliation	1.Chinese Academy of Sciences; Academy of Mathematics and Systems Science 2.Microsoft Research Asia 3.CERN, EP Department 4.Peking University; School of Physics 5.University of Chinese Academy of Sciences; School of Mathematical Sciences
Recommended Citation GB/T 7714	Gong,Shiqi,Meng,Qi,Zhang,Jue,et al. An efficient Lorentz equivariant graph neural network for jet tagging[J]. Journal of High Energy Physics,2022,2022(7).
APA	Gong,Shiqi.,Meng,Qi.,Zhang,Jue.,Qu,Huilin.,Li,Congqiao.,...&Liu,Tie-Yan.(2022).An efficient Lorentz equivariant graph neural network for jet tagging.Journal of High Energy Physics,2022(7).
MLA	Gong,Shiqi,et al."An efficient Lorentz equivariant graph neural network for jet tagging".Journal of High Energy Physics 2022.7(2022).

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