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Abstract:
Machine learning methods especially deep learning have been becoming popular on jet representation in particle physics. Most of these methods focus on the handcrafted feature design or tuning the structure of existing black-box deep neural networks, while they ignore the Lorentz group equivariance, a fundamental space time symmetry in the law for jet generating. Recently, there is Lorentz equivariant deep model proposed for jet tagging, however, it is time-consuming due to the explicit reliance on the high-order tensors. In this paper, we propose an efficient LorentzNet, which directly scalarizes the tensors to realize Lorentz group symmetry. Experiments on two representative jet tagging benchmarks show that LorentNet can achieve the best tagging accuracy under both clean and Lorentz rotated test setting.
About the speaker:
Dr. Qi Meng got her PhD from Peking University in 2018. She is now a Principal Researcher of the Microsoft Research AI4Science Lab. Her main research interest focuses on the AI theory as well as AI for Science. She has published more than 30 papers in prestigious AI Journals. She is also a referee for multiple Journals such as ICML, Neurips and TPAMI.
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