https://doi.org/10.1140/epjc/s10052-023-12293-1
Regular Article - Theoretical Physics
Quark/gluon discrimination and top tagging with dual attention transformer
1
University of Chinese Academy of Sciences, 100049, Beijing, People’s Republic of China
2
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, 100190, Beijing, People’s Republic of China
3
Department of Physics, Konkuk University, 05029, Seoul, Republic of Korea
Received:
18
July
2023
Accepted:
27
November
2023
Published online:
8
December
2023
Jet tagging is a crucial classification task in high energy physics. Recently the performance of jet tagging has been significantly improved by the application of deep learning techniques. In this study, we introduce a new architecture for jet tagging: the particle dual attention transformer (P-DAT). This novel transformer architecture stands out by concurrently capturing both global and local information, while maintaining computational efficiency. Regarding the self attention mechanism, we have extended the established attention mechanism between particles to encompass the attention mechanism between particle features. The particle attention module computes particle level interactions across all the particles, while the channel attention module computes attention scores between particle features, which naturally captures jet level interactions by taking all particles into account. These two kinds of attention mechanisms can complement each other. Further, we incorporate both the pairwise particle interactions and the pairwise jet feature interactions in the attention mechanism. We demonstrate the effectiveness of the P-DAT architecture in classic top tagging and quark–gluon discrimination tasks, achieving competitive performance compared to other benchmark strategies.
© The Author(s) 2023
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