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Does Lorentz-symmetric design boost network performance in jet physics? / Li, Congqiao (Peking U.) ; Qu, Huilin (CERN) ; Qian, Sitian (Peking U.) ; Meng, Qi (Unlisted) ; Gong, Shiqi (Beijing, Acad. Math. Syst. Sci. ; LSEC, Beijing) ; Zhang, Jue (Unlisted) ; Liu, Tie-Yan (Unlisted) ; Li, Qiang (Peking U.)
In the deep learning era, improving the neural network performance in jet physics is a rewarding task as it directly contributes to more accurate physics measurements at the LHC. Recent research has proposed various network designs in consideration of the full Lorentz symmetry, but its benefit is still not systematically asserted, given that there remain many successful networks without taking it into account. [...]
arXiv:2208.07814.- 2024-03-01 - 15 p. - Published in : Phys. Rev. D 109 (2024) 056003 Fulltext: 2208.07814 - PDF; Publication - PDF;

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An Efficient Lorentz Equivariant Graph Neural Network for Jet Tagging / Gong, Shiqi (Beijing, Acad. Math. Syst. Sci.) ; Meng, Qi (Unlisted) ; Zhang, Jue (Unlisted) ; Qu, Huilin (CERN) ; Li, Congqiao (Peking U.) ; Qian, Sitian (Peking U.) ; Du, Weitao (Beijing, Acad. Math. Syst. Sci.) ; Ma, Zhi-Ming (Beijing, Acad. Math. Syst. Sci.) ; Liu, Tie-Yan (Unlisted)
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. [...]
arXiv:2201.08187.- 2022-07-05 - 22 p. - Published in : JHEP 2207 (2022) 030 Fulltext: document - PDF; 2201.08187 - PDF;

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