A review of transformer-based jet tagging that highlights the authors' CA-Mixer network as a state-of-the-art, faster alternative to Particle Transformer.
An Attention Based Neural Network for Jet Tagging
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abstract
Convolutional neural networks are basic structures using jet images as input for the jet tagging problems. However, what they have learned during the training process is always difficult to understand just through feature maps. Inspired by the attention mechanism popular in machine learning fields, we propose a novel attention-based neural network (ABNN) to get insight of this problem. The ABNN combines a jet image with average jet images from the signal and the background to generate attention maps which show clearly the relevant importance according to the different origination of jets. Compared with networks in the similar architecture, this network achieves better performance, which indicates the potential of attention mechanism to use in other works.
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hep-ph 1years
2024 1verdicts
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Transformer networks for Heavy flavor jet tagging
A review of transformer-based jet tagging that highlights the authors' CA-Mixer network as a state-of-the-art, faster alternative to Particle Transformer.