180 citations · 596 across the 15 of their papers we have counts for
Showing 2022Show all
3 papers · 1 filter
hep-ph2022★ 11 cited
Does Lorentz-symmetric design boost network performance in jet physics?
Congqiao Li, Huilin Qu, Sitian Qian +5
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. R…
hep-ph2022★ 47 cited
Particle Transformer for Jet Tagging
Huilin Qu, Congqiao Li, Sitian Qian
Jet tagging is a critical yet challenging classification task in particle physics. While deep learning has transformed jet tagging and significantly improved performance, the lack…
hep-ph2022★ 121 cited
An Efficient Lorentz Equivariant Graph Neural Network for Jet Tagging
Shiqi Gong, Qi Meng, Jue Zhang +6
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 th…