Jet tagging with more-interaction particle transformer  

作　　者：Yifan Wu Kun Wang Congqiao Li Huilin Qu Jingya Zhu 吴佚凡;王坤;李聪乔;曲慧麟;朱经亚(College of Science,University of Shanghai for Science and Technology,Shanghai 200093,China;School of Physics and State Key Laboratory of Nuclear Physics and Technology,Peking University,Beijing 100871,China;CERN,EP Department,CH-1211 Geneva 23,Switzerland;School of Physics and Electronics,Henan University,Kaifeng 475004,China)

机构地区：[1]College of Science,University of Shanghai for Science and Technology,Shanghai 200093,China [2]School of Physics and State Key Laboratory of Nuclear Physics and Technology,Peking University,Beijing 100871,China [3]CERN,EP Department,CH-1211 Geneva 23,Switzerland [4]School of Physics and Electronics,Henan University,Kaifeng 475004,China

出　　处：《Chinese Physics C》2025年第1期164-176,共13页中国物理C(英文版)

基　　金：Supported by the National Natural Science Foundation of China(12275066,11605123)。

摘　　要：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 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 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 efficiency 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 embeddings,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 increase the efficiency of benchmarks for jet tagging and event identification in particle physics.

关 键 词：jet tagging collider physics machine learning 

分 类 号：O57[理学—粒子物理与原子核物理]