Jet Charge and Machine Learning

Abstract

Modern machine learning techniques, such as convolutional, recurrent and recursive neural networks, have shown promise for jet substructure at the Large Hadron Collider. For example, they have demonstrated effectiveness at boosted top or W boson identification or for quark/gluon discrimination. We explore these methods for the purpose of classifying jets according to their electric charge. We find that both neural networks that incorporate distance within the jet as an input and boosted decision trees including radial distance information can provide significant improvement in jet charge extraction over current methods. Specifically, convolutional, recurrent, and recursive networks can provide the largest improvement over traditional methods, in part by effectively utilizing distance within the jet or clustering history. The advantages of using a fixed-size input representation (as with the CNN) or a small input representation (as with the RNN) suggest that both convolutional and recurrent networks will be essential to the future of modern machine learning at colliders.

arXiv comment17 pages, 8 figures, 1 table; Updated to JHEP version

References in corpus (23)

• A Brief Introduction to PYTHIA 8.1
• Top-tagging: A Method for Identifying Boosted Hadronic Tops
• Towards Jetography
• Maximizing Boosted Top Identification by Minimizing N-subjettiness
• Classification without labels: Learning from mixed samples in high energy physics
• 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
• Substructure of high-p_T Jets at the LHC
• Quark and Gluon Tagging 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
• Quark and Gluon Jet Substructure
• Jet Charge at the LHC
• Pileup Mitigation with Machine Learning (PUMML)
• Learning to Classify from Impure Samples with High-Dimensional Data
• Calculating the Charge of a Jet
• Multivariate discrimination and the Higgs + W/Z search
• Recursive Neural Networks in Quark/Gluon Tagging
• W-jet Tagging: Optimizing the Identification of Boosted Hadronically-Decaying W Bosons
• (Machine) Learning to Do More with Less

Cited by in corpus (51)

• ParticleNet: Jet Tagging via Particle Clouds
• Energy Flow Networks: Deep Sets for Particle Jets
• Learning New Physics from a Machine
• Extending the Bump Hunt with Machine Learning
• QCD or What?
• Adversarially-trained autoencoders for robust unsupervised new physics searches
• QCD-Aware Recursive Neural Networks for Jet Physics
• An Efficient Lorentz Equivariant Graph Neural Network for Jet Tagging
• Uncovering latent jet substructure
• DisCo Fever: Robust Networks Through Distance Correlation
• ABCDisCo: Automating the ABCD Method with Machine Learning
• Quark-Gluon Tagging: Machine Learning vs Detector
• Higgs bosons with large couplings to light quarks
• An operational definition of quark and gluon jets
• A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty
• Supervised Jet Clustering with Graph Neural Networks for Lorentz Boosted Bosons
• Exploring SMEFT in VH with Machine Learning
• A Theory of Quark vs. Gluon Discrimination
• Boosted $W/Z$ Tagging with Jet Charge and Deep Learning
• Automating the Construction of Jet Observables with Machine Learning
• Topological Obstructions to Autoencoding
• Mass Agnostic Jet Taggers
• Infrared Safety of a Neural-Net Top Tagging Algorithm
• Jet Flavour Tagging for Future Colliders with Fast Simulation
• Binary JUNIPR: an interpretable probabilistic model for discrimination
• Higgs boson tagging with the Lund jet plane
• Jet charge modification in dense QCD matter
• Probing the $CP$ nature of the top quark Yukawa at hadron colliders
• Machine learning-based jet and event classification at the Electron-Ion Collider with applications to hadron structure and spin physics
• Machine Learning in High Energy Physics: A review of heavy-flavor jet tagging at the LHC
• Classification of Fermi-LAT sources with deep learning using energy and time spectra
• Jet tagging algorithm of graph network with HaarPooling message passing
• Dynamic Jet Charge
• The Radiation Valley and Exotic Resonances in $W\gamma$ Production at the LHC
• Discriminating between Higgs Production Mechanisms via Jet Charge at the LHC
• Quark jet rates and quark/gluon discrimination in multi-jet final states
• Learning to Inflate
• Sensing Higgs cascade decays through memory
• Beyond $M_{t\bar{t}}$: learning to search for a broad $t\bar{t}$ resonance at the LHC
• Is infrared-collinear safe information all you need for jet classification?
• Probing Higgs exotic decay at the LHC with machine learning
• Search for Mono-Higgs Signals in $b\bar{b}$ Final States Using Deep Neural Networks
• Deep Learning Jet Substructure from Two-Particle Correlation
• Machine learning classification: case of Higgs boson CP state in H to tau tau decay at LHC
• Analytical Insights on Hadronic Top Quark Polarimetry
• Classify the Higgs decays with the PFN and ParticleNet at electron-positron colliders
• Lepton Flavor Violation: From Muon Decays to Muon Colliders
• Improving Heavy Dijet Resonance Searches Using Jet Substructure at the LHC
• Identifying the Quantum Properties of Hadronic Resonances using Machine Learning
• Stable and Interpretable Jet Physics with IRC-Safe Equivariant Feature Extraction
• Jet Charge with Global Event Shapes: Probing Quark Flavor Dynamics