Jet Tagging with More-Interaction Particle Transformer

Abstract

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 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% 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 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 find 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 the fine-tuned MIParT-L improved by 39% on the top tagging dataset and by 6% on the quark-gluon dataset, surpassing that of the fine-tuned ParT. Specifically, the background rejection of fine-tuned MIParT-L improved by an additional 2% compared to the fine-tuned ParT. The results suggest that MIParT has the potential to advance efficiency benchmarks for jet tagging and event identification in particle physics. The code is available at the following GitHub repository: https://github.com/USST-HEP/MIParT

12 pages, 6 figures, 5 tables. The code is available at the following GitHub repository: https://github.com/USST-HEP/MIParT

References in corpus (27)

• ParticleNet: Jet Tagging via Particle Clouds
• Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning
• Deep learning in color: towards automated quark/gluon jet discrimination
• Jet-Images -- Deep Learning Edition
• Deep-learning Top Taggers or The End of QCD?
• Energy Flow Networks: Deep Sets for Particle Jets
• The Machine Learning Landscape of Top Taggers
• Jet Flavour Classification Using DeepJet
• Gaining (Mutual) Information about Quark/Gluon Discrimination
• Quark and Gluon Tagging at the LHC
• Jet Flavor Classification in High-Energy Physics with Deep Neural Networks
• Jet-Images: Computer Vision Inspired Techniques for Jet Tagging
• Systematics of quark/gluon tagging
• Quark and Gluon Jet Substructure
• Pulling Out All the Tops with Computer Vision and Deep Learning
• QCD-Aware Recursive Neural Networks for Jet Physics
• An Efficient Lorentz Equivariant Graph Neural Network for Jet Tagging
• ABCNet: An attention-based method for particle tagging
• Casimir Meets Poisson: Improved Quark/Gluon Discrimination with Counting Observables
• Boosting $H\to b\bar{b}$ with Machine Learning
• Recursive Neural Networks in Quark/Gluon Tagging
• Probing stop pair production at the LHC with graph neural networks
• Unveiling CP property of top-Higgs coupling with graph neural networks at the LHC
• Quark-Gluon tagging with Shower Deconstruction: Unearthing dark matter and Higgs couplings
• Point Cloud Transformers applied to Collider Physics
• Associated jet and subjet rates in light-quark and gluon jet discrimination
• Search for Higgs boson decay to a charm quark-antiquark pair in proton-proton collisions at $\sqrt{s}$ = 13 TeV

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