424 citations · 912 across the 15 of their papers we have counts for
4 papers · 1 filter
Optimal transport for a novel event description at hadron colliders
Loukas Gouskos, Fabio Iemmi, Sascha Liechti +3
We propose a novel strategy for disentangling proton collisions at hadron colliders such as the LHC that considerably improves over the current state of the art. Employing a metric…
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…
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…
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…