Deep-learned Top Tagging with a Lorentz Layer

Abstract

We introduce a new and highly efficient tagger for hadronically decaying top quarks, based on a deep neural network working with Lorentz vectors and the Minkowski metric. With its novel machine learning setup and architecture it allows us to identify boosted top quarks not only from calorimeter towers, but also including tracking information. We show how the performance of our tagger compares with QCD-inspired and image-recognition approaches and find that it significantly increases the performance for strongly boosted top quarks.

v3: minor revisions following SciPost referee reports

References in corpus (17)

• An Introduction to PYTHIA 8.2
• Jet substructure as a new Higgs search channel at the LHC
• Top-tagging: A Method for Identifying Boosted Hadronic Tops
• Pileup Per Particle Identification
• Boosted objects: a probe of beyond the Standard Model physics
• Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis
• Deep learning in color: towards automated quark/gluon jet discrimination
• Deep-learning Top Taggers or The End of QCD?
• Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks
• Top Jets at the LHC
• Pileup Mitigation with Machine Learning (PUMML)
• Color-octet scalars at the LHC
• Template Overlap Method for Massive Jets
• How Much Information is in a Jet?
• Using jet mass to discover vector quarks at the LHC
• W-jet Tagging: Optimizing the Identification of Boosted Hadronically-Decaying W Bosons
• Tagging Boosted Ws with Wavelets

Cited by in corpus (103)

• A trans-disciplinary review of deep learning research for water resources scientists
• Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems
• 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
• Energy Flow Networks: Deep Sets for Particle Jets
• The Machine Learning Landscape of Top Taggers
• Learning New Physics from a Machine
• Extending the Bump Hunt with Machine Learning
• Graph Neural Networks in Particle Physics
• QCD or What?
• Novelty Detection Meets Collider Physics
• Adversarially-trained autoencoders for robust unsupervised new physics searches
• Pulling Out All the Tops with Computer Vision and Deep Learning
• Solving differential equations with neural networks: Applications to the calculation of cosmological phase transitions
• 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
• Uncovering latent jet substructure
• Performance of top-quark and $W$-boson tagging with ATLAS in Run 2 of the LHC
• Autoencoders for unsupervised anomaly detection in high energy physics
• Tag N' Train: A Technique to Train Improved Classifiers on Unlabeled Data
• Neural Networks for Full Phase-space Reweighting and Parameter Tuning
• Boosting $H\to b\bar{b}$ with Machine Learning
• DisCo Fever: Robust Networks Through Distance Correlation
• 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
• FPGA-accelerated machine learning inference as a service for particle physics computing
• ABCDisCo: Automating the ABCD Method with Machine Learning
• Quark-Gluon Tagging: Machine Learning vs Detector
• 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
• Deep-Learning Jets with Uncertainties and More
• Casting a graph net to catch dark showers
• Supervised deep learning in high energy phenomenology: a mini review
• Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge
• Supervised Jet Clustering with Graph Neural Networks for Lorentz Boosted Bosons
• Interpretable Deep Learning for Two-Prong Jet Classification with Jet Spectra
• PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics
• Equivariant Energy Flow Networks for Jet Tagging
• Symmetries, Safety, and Self-Supervision
• Lorentz Boost Networks: Autonomous Physics-Inspired Feature Engineering
• Boosted $W/Z$ Tagging with Jet Charge and Deep Learning
• Reweighting a parton shower using a neural network: the final-state case
• Per-Object Systematics using Deep-Learned Calibration
• Automating the Construction of Jet Observables with Machine Learning
• Measuring QCD Splittings with Invertible Networks
• Topological Obstructions to Autoencoding
• Mass Agnostic Jet Taggers
• Energy-weighted Message Passing: an infra-red and collinear safe graph neural network algorithm
• 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
• Neural Network-based Top Tagger with Two-Point Energy Correlations and Geometry of Soft Emissions
• Reports of My Demise Are Greatly Exaggerated: $N$-subjettiness Taggers Take On Jet Images
• Explainable Equivariant Neural Networks for Particle Physics: PELICAN
• Lorentz group equivariant autoencoders
• 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
• Topology classification with deep learning to improve real-time event selection at the LHC
• Combine and Conquer: Event Reconstruction with Bayesian Ensemble Neural Networks
• IRC-safe Graph Autoencoder for unsupervised anomaly detection
• Deep learning for the R-parity violating supersymmetry searches at the LHC
• CapsNets Continuing the Convolutional Quest
• Six Top Messages of New Physics at the LHC
• Jet grooming through reinforcement learning
• SHAPER: Can You Hear the Shape of a Jet?
• Invisible Higgs search through Vector Boson Fusion: A deep learning approach
• A Detailed Study of Interpretability of Deep Neural Network based Top Taggers
• Leveraging universality of jet taggers through transfer learning
• A Lorentz-Equivariant Transformer for All of the LHC
• Exploring Jet Substructure in Semi-visible jets
• On the BSM reach of four top production at the LHC
• Machine learning phases and criticalities without using real data for training
• Does Lorentz-symmetric design boost network performance in jet physics?
• Maximum performance of strange-jet tagging at hadron colliders
• Towards recognizing the light facet of the Higgs Boson
• Jets with electrons from boosted top quarks
• Is infrared-collinear safe information all you need for jet classification?
• Comparing Point Cloud Strategies for Collider Event Classification
• Reconstruction of decays to merged photons using end-to-end deep learning with domain continuation in the CMS detector
• Pileup and Infrared Radiation Annihilation (PIRANHA): A Paradigm for Continuous Jet Grooming
• Deep Learning Jet Substructure from Two-Particle Correlation
• Boosted top tagging and its interpretation using Shapley values
• Semi-Equivariant GNN Architectures for Jet Tagging
• Attention to the strengths of physical interactions: Transformer and graph-based event classification for particle physics experiments
• End-to-End Jet Classification of Boosted Top Quarks with the CMS Open Data
• Lorentz and permutation invariants of particles I
• Unveiling the Secrets of New Physics Through Top Quark Tagging
• Interplay of Traditional Methods and Machine Learning Algorithms for Tagging Boosted Objects
• Investigating top tagging with Y${}_{\text{m}}$-Splitter and N-subjettiness
• Jet Single Shot Detection
• Top-philic Machine Learning
• Lorentz and permutation invariants of particles III: constraining non-standard sources of parity violation
• Learning Broken Symmetries with Approximate Invariance
• Identifying the Quantum Properties of Hadronic Resonances using Machine Learning
• Solving Combinatorial Problems at Particle Colliders Using Machine Learning
• Graph theory inspired anomaly detection at the LHC
• Large Language Models -- the Future of Fundamental Physics?
• Theory, phenomenology, and experimental avenues for dark showers: a Snowmass 2021 report
• Stable and Interpretable Jet Physics with IRC-Safe Equivariant Feature Extraction
• Tagging ultra-boosted jets at FCC-hh using machine learning techniques