Abstract
In this paper,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 Atten-tion(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 and a series of Lorentz-equivariant methods,but also significantly outperforms the ParT model in background rejection.Specifically,it improves background rejection by approximately 25％with a signal effi-ciency of 30％on the top tagging dataset and by 3％on the quark-gluon dataset.Additionally,MIParT requires only 30％of the parameters and 53％of the computational complexity needed by ParT,proving that high performance can be achieved with reduced model complexity.For very large datasets,we double the dimension of particle embed-dings,referring to this variant as MIParT-Large(MIParT-L).We found that MIParT-L can further capitalize on the knowledge from large datasets.From a model pre-trained on the 100M JetClass dataset,the background rejection performance of fine-tuned MIParT-L improves by 39％on the top tagging dataset and by 6％on the quark-gluon dataset,surpassing that of fine-tuned ParT.Specifically,the background rejection of fine-tuned MIParT-L improves by an additional 2％compared to that of fine-tuned ParT.These results suggest that MIParT has the potential to in-crease the efficiency of benchmarks for jet tagging and event identification in particle physics.
NETL
NSTL
万方数据