An efficient Lorentz equivariant graph neural network for jet tagging

doi:10.1007/JHEP07(2022)030

	An efficient Lorentz equivariant graph neural network for jet tagging
	Gong,Shiqi 1,5; Meng,Qi 4; Zhang,Jue 4; Qu,Huilin 3; Li,Congqiao 2; Qian,Sitian 2; Du,Weitao 5; Ma,Zhi-Ming 5; Liu,Tie-Yan 4
刊名	Journal of High Energy Physics
	2022-07-05
卷号	2022 期号:7
关键词	Jets and Jet Substructure Top Quark
DOI	10.1007/JHEP07(2022)030
英文摘要	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.
语种	英语
出版者	Springer Berlin Heidelberg
WOS记录号	BMC:10.1007/JHEP07(2022)030
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/60420]
专题	中国科学院数学与系统科学研究院
通讯作者	Meng,Qi
作者单位	1.University of Chinese Academy of Sciences; School of Mathematical Sciences 2.Peking University; School of Physics 3.CERN, EP Department 4.Microsoft Research Asia 5.Chinese Academy of Sciences; Academy of Mathematics and Systems Science
推荐引用方式 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).