Supervised Jet Clustering with Graph Neural Networks for Lorentz Boosted Bosons

Abstract

Jet clustering is traditionally an unsupervised learning task because there is no unique way to associate hadronic final states with the quark and gluon degrees of freedom that generated them. However, for uncolored particles like $W$, $Z$, and Higgs bosons, it is possible to approximately (though not exactly) associate final state hadrons to their ancestor. By labeling simulated final state hadrons as descending from an uncolored particle, it is possible to train a supervised learning method to create boson jets. Such a method much operates on individual particles and identifies connections between particles originating from the same uncolored particle. Graph neural networks are well-suited for this purpose as they can act on unordered sets and naturally create strong connections between particles with the same label. These networks are used to train a supervised jet clustering algorithm. The kinematic properties of these graph jets better match the properties of simulated Lorentz-boosted $W$ bosons. Furthermore, the graph jets contain more information for discriminating $W$ jets from generic quark jets. This work marks the beginning of a new exploration in jet physics to use machine learning to optimize the construction of jets and not only the observables computed from jet constituents.

arXiv comment12 pages, 8 figures, data is published at https://zenodo.org/record/3981290#.XzQs5zVlAUF, code is available at https://github.com/xju2/root_gnn/releases/tag/v0.6.0

References in corpus (96)

• Adam: A Method for Stochastic Optimization
• PYTHIA 6.4 Physics and Manual
• PYTHIA 6.2 Physics and Manual
• The anti-k_t jet clustering algorithm
• A Brief Introduction to PYTHIA 8.1
• FastJet user manual
• Dispelling the N^3 myth for the Kt jet-finder
• Searching for Exotic Particles in High-Energy Physics with Deep Learning
• Jet substructure as a new Higgs search channel at the LHC
• Identifying Boosted Objects with N-subjettiness
• Soft Drop
• Jet Trimming
• Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV
• Interaction Networks for Learning about Objects, Relations and Physics
• Towards an understanding of jet substructure
• Towards Jetography
• ParticleNet: Jet Tagging via Particle Clouds
• Deep Learning and its Application to LHC Physics
• Maximizing Boosted Top Identification by Minimizing N-subjettiness
• 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
• Recombination Algorithms and Jet Substructure: Pruning as a Tool for Heavy Particle Searches
• Jet Substructure at the Tevatron and LHC: New results, new tools, new benchmarks
• Jet Substructure at the Large Hadron Collider: Experimental Review
• 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
• Power Counting to Better Jet Observables
• Boosted objects and jet substructure at the LHC
• Deep-learned Top Tagging with a Lorentz Layer
• 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
• Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
• Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks
• Energy flow polynomials: A complete linear basis for jet substructure
• Learning representations of irregular particle-detector geometry with distance-weighted graph networks
• Dijet resonance search with weak supervision using $\sqrt{s}=13$ TeV $pp$ collisions in the ATLAS detector
• Pulling Out All the Tops with Computer Vision and Deep Learning
• JEDI-net: a jet identification algorithm based on interaction networks
• QCD-Aware Recursive Neural Networks for Jet Physics
• Pileup Mitigation with Machine Learning (PUMML)
• Performance of top-quark and $W$-boson tagging with ATLAS in Run 2 of the LHC
• Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics
• Learning to Classify from Impure Samples with High-Dimensional Data
• Search for low mass vector resonances decaying into quark-antiquark pairs in proton-proton collisions at sqrt(s) = 13 TeV
• Inclusive search for a highly boosted Higgs boson decaying to a bottom quark-antiquark pair
• Boosting $H\to b\bar{b}$ with Machine Learning
• How Much Information is in a Jet?
• Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors
• A Deep Neural Network for Pixel-Level Electromagnetic Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber
• Recursive Neural Networks in Quark/Gluon Tagging
• A Living Review of Machine Learning for Particle Physics
• Novel Jet Observables from Machine Learning
• In situ calibration of large-$R$ jet energy and mass in 13 TeV proton-proton collisions with the ATLAS detector
• Search for heavy diboson resonances in semileptonic final states in $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• Interaction networks for the identification of boosted $H\to b\overline{b}$ decays
• Jet Charge and Machine Learning
• Probing stop pair production at the LHC with graph neural networks
• Search for light resonances decaying to boosted quark pairs and produced in association with a photon or a jet in proton-proton collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• Quark-Gluon Tagging: Machine Learning vs Detector
• Unveiling CP property of top-Higgs coupling with graph neural networks at the LHC
• A deep neural network to search for new long-lived particles decaying to jets
• Search for a massive resonance decaying to a pair of Higgs bosons in the four b quark final state in proton-proton collisions at $\sqrt{s}=$ 13 TeV
• Optimisation of large-radius jet reconstruction for the ATLAS detector in 13 TeV proton-proton collisions
• Interpretable Deep Learning for Two-Prong Jet Classification with Jet Spectra
• Search for diboson resonances in hadronic final states in 139 fb${}^{-1}$ of $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• Boosted $W/Z$ Tagging with Jet Charge and Deep Learning
• Search for low mass vector resonances decaying to quark-antiquark pairs in proton-proton collisions at sqrt(s) = 13 TeV
• End-to-End Jet Classification of Quarks and Gluons with the CMS Open Data
• A multi-dimensional search for new heavy resonances decaying to boosted WW, WZ, or ZZ boson pairs in the dijet final state at 13 TeV
• Search for low mass vector resonances decaying into quark-antiquark pairs in proton-proton collisions at $\sqrt{s}=$ 13 TeV
• Identification of boosted Higgs bosons decaying into $b$-quark pairs with the ATLAS detector at 13 TeV
• Search for low-mass resonances decaying into bottom quark-antiquark pairs in proton-proton collisions at $\sqrt{s}=$ 13 TeV
• Automating the Construction of Jet Observables with Machine Learning
• Search for resonances decaying into a weak vector boson and a Higgs boson in the fully hadronic final state produced in proton$-$proton collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• Neural Network-based Top Tagger with Two-Point Energy Correlations and Geometry of Soft Emissions
• Electromagnetic Showers Beyond Shower Shapes
• Search for low-mass quark-antiquark resonances produced in association with a photon at $\sqrt{s}=$ 13 TeV
• Combination of CMS searches for heavy resonances decaying to pairs of bosons or leptons
• Probing triple Higgs coupling with machine learning at the LHC
• Search for Higgs boson decays into a $Z$ boson and a light hadronically decaying resonance using 13 TeV $pp$ collision data from the ATLAS detector
• Investigating the Topology Dependence of Quark and Gluon Jets
• Topology classification with deep learning to improve real-time event selection at the LHC
• Search for heavy resonances decaying into two Higgs bosons or into a Higgs boson and a W or Z boson in proton-proton collisions at 13 TeV
• CapsNets Continuing the Convolutional Quest
• Search for resonances decaying to a pair of Higgs bosons in the $\mathrm{b}\overline{\mathrm{b}}\mathrm{q}{\overline{\mathrm{q}}}^{\prime }\ell \nu$ final state in proton-proton collisions at $\sqrt{s}=$ 13 TeV
• Reconstruction and identification of boosted di-$\tau$ systems in a search for Higgs boson pairs using 13 TeV proton$-$proton collision data in ATLAS
• Optimal Jet Finder
• A search for resonances decaying into a Higgs boson and a new particle $X$ in the $XH\to qqbb$ final state with the ATLAS detector
• Fuzzy Jets
• Track Seeding and Labelling with Embedded-space Graph Neural Networks
• Scalable, Proposal-free Instance Segmentation Network for 3D Pixel Clustering and Particle Trajectory Reconstruction in Liquid Argon Time Projection Chambers
• Strange Jet Tagging

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• Anomaly detection with Convolutional Graph Neural Networks
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• Symmetries, Safety, and Self-Supervision
• Equivariant Energy Flow Networks for Jet Tagging
• The Boosted Higgs Jet Reconstruction via Graph Neural Network
• Unleashing the full power of LHCb to probe Stealth New Physics
• Shared Data and Algorithms for Deep Learning in Fundamental Physics
• Machine Learning in High Energy Physics: A review of heavy-flavor jet tagging at the LHC
• Combine and Conquer: Event Reconstruction with Bayesian Ensemble Neural Networks
• Topological Reconstruction of Particle Physics Processes using Graph Neural Networks
• Machine Learning for Particle Flow Reconstruction at CMS
• Reconstructing boosted Higgs jets from event image segmentation
• Improved Constraints on Effective Top Quark Interactions using Edge Convolution Networks
• Jet Discrimination with Quantum Complete Graph Neural Network
• Does Lorentz-symmetric design boost network performance in jet physics?
• Maximum performance of strange-jet tagging at hadron colliders
• BIP: Boost Invariant Polynomials for Efficient Jet Tagging
• Real-time Graph Building on FPGAs for Machine Learning Trigger Applications in Particle Physics
• Reconstruction of Unstable Heavy Particles Using Deep Symmetry-Preserving Attention Networks
• Reconstruction of decays to merged photons using end-to-end deep learning with domain continuation in the CMS detector
• Higgs and top physics reconstruction challenges and opportunities at FCC-ee
• 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
• Spectral Clustering for Jet Physics
• Top-philic Machine Learning
• PAIReD jet: A multi-pronged resonance tagging strategy across all Lorentz boosts
• PASCL: Supervised Contrastive Learning with Perturbative Augmentation for Particle Decay Reconstruction
• Jet Reconstruction with Mamba Networks in Collider Events
• Deep learning for jet modification in the presence of the QGP background
• Search for exotic Higgs boson decays H $\to$ $\mathcal{AA}$ with $\mathcal{AA}$ $\to$ $\gamma \gamma$ in events with a semi-merged topology in proton-proton collisions at $\sqrt{s}$ = 13 TeV