  In this study, we introduce the More-Interaction Particle Transformer
(MIParT), a novel deep learning neural network designed for jet tagging. This
framework incorporates our own design, the More-Interaction Attention (MIA)
mechanism, which increases the dimensionality of particle interaction
embeddings. We tested MIParT using the top tagging and quark-gluon datasets.
Our results show that MIParT not only matches the accuracy and AUC of
LorentzNet but also significantly outperforms the ParT model in background
rejection. Specifically, it improves background rejection by approximately 25\%
at a 30\% signal efficiency on the top tagging dataset and by 3\% on the
quark-gluon dataset. Additionally, MIParT requires only 30% of the parameters
and 47% of the computational complexity needed by ParT, proving that high
performance can be achieved with reduced model complexity and without extensive
pretraining on large datasets. The results suggest that MIParT has the
potential to advance efficiency benchmarks for jet tagging and event
identification in particle physics.
