  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.
