The Boosted Higgs Jet Reconstruction via Graph Neural Network

Abstract

By representing each collider event as a point cloud, we adopt the Graphic Convolutional Network (GCN) with focal loss to reconstruct the Higgs jet in it. This method provides higher Higgs tagging efficiency and better reconstruction accuracy than the traditional methods which use jet substructure information. The GCN, which is trained on events of the $H$+jets process, is capable of detecting a Higgs jet in events of several different processes, even though the performance degrades when there are boosted heavy particles other than the Higgs in the event. We also demonstrate the signal and background discrimination capacity of the GCN by applying it to the $t\bar{t}$ process. Taking the outputs of the network as new features to complement the traditional jet substructure variables, the $t\bar{t}$ events can be separated further from the $H$+jets events.

arXiv comment18 pages, 8 figures, version accepted for publication in PRD

References in corpus (35)

• The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
• The anti-k_t jet clustering algorithm
• A Brief Introduction to PYTHIA 8.1
• Focal Loss for Dense Object Detection
• Better Jet Clustering Algorithms
• Jet substructure as a new Higgs search channel at the LHC
• Tuning PYTHIA 8.1: the Monash 2013 Tune
• Identifying Boosted Objects with N-subjettiness
• Jet Trimming
• Combined measurements of Higgs boson production and decay using up to $80$ fb${}^{-1}$ of proton-proton collision data at $\sqrt{s}=$ 13 TeV collected with the ATLAS experiment
• Combined measurements of Higgs boson couplings in proton-proton collisions at $\sqrt{s}=$ 13 TeV
• ParticleNet: Jet Tagging via Particle Clouds
• Deep Learning and its Application to LHC Physics
• Jet substructure as a new Higgs search channel at the LHC
• Boosted objects: a probe of beyond the Standard Model physics
• Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning
• Techniques for improved heavy particle searches with jet substructure
• Jet Substructure at the Tevatron and LHC: New results, new tools, new benchmarks
• Jet Substructure at the Large Hadron Collider: Experimental Review
• Energy Flow Networks: Deep Sets for Particle Jets
• Boosted objects and jet substructure at the LHC
• Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
• JEDI-net: a jet identification algorithm based on interaction networks
• Charged-particle distributions in $\sqrt{s}=13$ TeV $pp$ interactions measured with the ATLAS detector at the LHC
• ABCNet: An attention-based method for particle tagging
• Boosting $H\to b\bar{b}$ with Machine Learning
• Energy Linearity and Resolution of the ATLAS Electromagnetic Barrel Calorimeter in an Electron Test-Beam
• Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors
• Interaction networks for the identification of boosted $H\to b\overline{b}$ decays
• Probing stop pair production at the LHC with graph neural networks
• Supervised Jet Clustering with Graph Neural Networks for Lorentz Boosted Bosons
• Interpretable Deep Learning for Two-Prong Jet Classification with Jet Spectra
• Novel deep learning methods for track reconstruction
• Automating the Construction of Jet Observables with Machine Learning
• Neural Network-based Top Tagger with Two-Point Energy Correlations and Geometry of Soft Emissions

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• Shared Data and Algorithms for Deep Learning in Fundamental Physics
• Topological Reconstruction of Particle Physics Processes using Graph Neural Networks
• Unbinned Deep Learning Jet Substructure Measurement in High $Q^2$ ep collisions at HERA
• Secondary Vertex Finding in Jets with Neural Networks
• Machine Learning for Particle Flow Reconstruction at CMS
• Deep Learning Jet Image as a Probe of Light Higgsino Dark Matter at the LHC
• Does Lorentz-symmetric design boost network performance in jet physics?
• Jet Discrimination with Quantum Complete Graph Neural Network
• Reconstruction of Unstable Heavy Particles Using Deep Symmetry-Preserving Attention Networks
• Automatic detection of boosted Higgs boson and top quark jets in an event image
• Interplay of Traditional Methods and Machine Learning Algorithms for Tagging Boosted Objects
• Top-philic Machine Learning
• PASCL: Supervised Contrastive Learning with Perturbative Augmentation for Particle Decay Reconstruction
• Scalable Multi-Task Learning for Particle Collision Event Reconstruction with Heterogeneous Graph Neural Networks
• Jet Reconstruction with Mamba Networks in Collider Events
• HL-LHC sensitivity to higgsinos from natural SUSY with gravitino LSP
• Deep learning for jet modification in the presence of the QGP background
• Probing new physics in the Boosted $HH\to b\bar{b}\gamma \gamma$ channel at the LHC