Jet tagging algorithm of graph network with HaarPooling message passing

Abstract

Recently methods of graph neural networks (GNNs) have been applied to solving the problems in high energy physics (HEP) and have shown its great potential for quark-gluon tagging with graph representation of jet events. In this paper, we introduce an approach of GNNs combined with a HaarPooling operation to analyze the events, called HaarPooling Message Passing neural network (HMPNet). In HMPNet, HaarPooling not only extracts the features of graph, but embeds additional information obtained by clustering of k-means of different particle features. We construct Haarpooling from five different features: absolute energy $\log E$, transverse momentum $\log p_T$, relative coordinates $(\Delta \eta ,\Delta \varphi )$, the mixed ones $(\log E,\log p_T)$ and $(\log E,\log p_T,\Delta \eta ,\Delta \varphi )$. The results show that an appropriate selection of information for HaarPooling enhances the accuracy of quark-gluon tagging, as adding extra information of $\log P_T$ to the HMPNet outperforms all the others, whereas adding relative coordinates information $(\Delta \eta ,\Delta \varphi )$ is not very effective. This implies that by adding effective particle features from HaarPooling can achieve much better results than solely pure message passing neutral network (MPNN) can do, which demonstrates significant improvement of feature extraction via the pooling process. Finally we compare the HMPNet study, ordering by $p_T$, with other studies and prove that the HMPNet is also a good choice of GNN algorithms for jet tagging.

References in corpus (45)

• PYTHIA 6.4 Physics and Manual
• PYTHIA 6.2 Physics and Manual
• The anti-k_t jet clustering algorithm
• An Introduction to PYTHIA 8.2
• FastJet user manual
• Particle-flow reconstruction and global event description with the CMS detector
• Top-tagging: A Method for Identifying Boosted Hadronic Tops
• Performance of $b$-Jet Identification in the ATLAS Experiment
• ParticleNet: Jet Tagging via Particle Clouds
• Jet reconstruction and performance using particle flow with the ATLAS Detector
• ATLAS $b$-jet identification performance and efficiency measurement with $t\bar{t}$ events in $pp$ collisions at $\sqrt{s}=13$ TeV
• Maximizing Boosted Top Identification by Minimizing N-subjettiness
• Deep learning in color: towards automated quark/gluon jet discrimination
• Jet-Images -- Deep Learning Edition
• Deep-learning Top Taggers or The End of QCD?
• Jet Substructure Classification in High-Energy Physics with Deep Neural Networks
• Energy Flow Networks: Deep Sets for Particle Jets
• 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
• Systematics of quark/gluon tagging
• Quark and Gluon Jet Substructure
• Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks
• Learning representations of irregular particle-detector geometry with distance-weighted graph networks
• Finding top quarks with shower deconstruction
• Pulling Out All the Tops with Computer Vision and Deep Learning
• The Lund Jet Plane
• JEDI-net: a jet identification algorithm based on interaction networks
• QCD-Aware Recursive Neural Networks for Jet Physics
• An Efficient Lorentz Equivariant Graph Neural Network for Jet Tagging
• Understanding Pooling in Graph Neural Networks
• ABCNet: An attention-based method for particle tagging
• Anomaly detection with Convolutional Graph Neural Networks
• Recursive Neural Networks in Quark/Gluon Tagging
• Jet Charge and Machine Learning
• 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
• How to Improve Top Tagging
• Quark-Gluon tagging with Shower Deconstruction: Unearthing dark matter and Higgs couplings
• Associated jet and subjet rates in light-quark and gluon jet discrimination
• Equivariant Energy Flow Networks for Jet Tagging
• Quarks and gluons in the Lund plane
• Benchmarking an Even Better HEPTopTagger
• IRC-safe Graph Autoencoder for unsupervised anomaly detection
• Leveraging universality of jet taggers through transfer learning

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