Infrared Safety of a Neural-Net Top Tagging Algorithm

Abstract

Neural network-based algorithms provide a promising approach to jet classification problems, such as boosted top jet tagging. To date, NN-based top taggers demonstrated excellent performance in Monte Carlo studies. In this paper, we construct a top-jet tagger based on a Convolutional Neural Network (CNN), and apply it to parton-level boosted top samples, with and without an additional gluon in the final state. We show that the jet observable defined by the CNN obeys the canonical definition of infrared safety: it is unaffected by the presence of the extra gluon, as long as it is soft or collinear with one of the quarks. Our results indicate that the CNN tagger is robust with respect to possible mis-modeling of soft and collinear final-state radiation by Monte Carlo generators.

arXiv comment7 pages, 8 figures, final version to be published in JHEP

References in corpus (23)

• A Brief Introduction to PYTHIA 8.1
• MadGraph 5 : Going Beyond
• Identifying Boosted Objects with N-subjettiness
• 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?
• Anomaly Detection for Resonant New Physics with Machine Learning
• Jet Substructure Classification in High-Energy Physics with Deep Neural Networks
• Deep-learned Top Tagging with a Lorentz Layer
• Sudakov Safety in Perturbative QCD
• Jet-Images: Computer Vision Inspired Techniques for Jet Tagging
• Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks
• Pulling Out All the Tops with Computer Vision and Deep Learning
• QCD-Aware Recursive Neural Networks for Jet Physics
• Learning to Classify from Impure Samples with High-Dimensional Data
• Unsafe but Calculable: Ratios of Angularities in Perturbative QCD
• Template Overlap Method for Massive Jets
• Recursive Neural Networks in Quark/Gluon Tagging
• Novel Jet Observables from Machine Learning
• Jet Charge and Machine Learning
• Three-particle templates for boosted Higgs

Cited by in corpus (26)

• ParticleNet: Jet Tagging via Particle Clouds
• Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning
• Searching for New Physics with Deep Autoencoders
• The Machine Learning Landscape of Top Taggers
• An Efficient Lorentz Equivariant Graph Neural Network for Jet Tagging
• Quark-Gluon Tagging: Machine Learning vs Detector
• Deep-Learning Jets with Uncertainties and More
• A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty
• Mapping Machine-Learned Physics into a Human-Readable Space
• Interpretable Deep Learning for Two-Prong Jet Classification with Jet Spectra
• Per-Object Systematics using Deep-Learned Calibration
• Energy-weighted Message Passing: an infra-red and collinear safe graph neural network algorithm
• Mass Agnostic Jet Taggers
• Jet Flavour Tagging for Future Colliders with Fast Simulation
• Neural Network-based Top Tagger with Two-Point Energy Correlations and Geometry of Soft Emissions
• Combine and Conquer: Event Reconstruction with Bayesian Ensemble Neural Networks
• CapsNets Continuing the Convolutional Quest
• Equivariant, Safe and Sensitive -- Graph Networks for New Physics
• Is infrared-collinear safe information all you need for jet classification?
• Hypergraphs in LHC Phenomenology -- The Next Frontier of IRC-Safe Feature Extraction
• Interplay of Traditional Methods and Machine Learning Algorithms for Tagging Boosted Objects
• Naturalness Sum Rules and Their Collider Tests
• Probing sub-TeV Higgsinos aided by a ML-based top tagger in the context of Trilinear RPV SUSY
• Foundations of automatic feature extraction at LHC--point clouds and graphs
• Top-philic Machine Learning
• Graph theory inspired anomaly detection at the LHC