The Machine Learning landscape of top taggers

2019/02/28 by G. Kasieczka, Gregor Kasieczka, Tilman Plehn +51 · 7 citations
Computer Science · Physics and Astronomy · #Computational Physics and Python Applications #Deep learning #Key (lock) #Particle physics theoretical and experimental studies #Quantum Chromodynamics and Particle Interactions #Range (aeronautics) #Task (project management) #Training set #hep-ph

paper · pdf · doi:10.21468/scipostphys.7.1.014

published as SciPost Phys. 7, 014 (2019) · Yet another tagger included!

openalex created_date 2019/03/02 · arxiv created 2019/07/23 · openalex publication_date 2019/07/30 · arxiv updated 2019/07/31 · openalex updated_date 2026/08/05

Abstract

Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches. Unlike most established methods they rely on low-level input, for instance calorimeter output. While their network architectures are vastly different, their performance is comparatively similar. In general, we find that these new approaches are extremely powerful and great fun.

Citations

• THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS
• The anti-ktjet clustering algorithm
• Top Tagging: A Method for Identifying Boosted Hadronically Decaying Top Quarks
• Top Tagging

Cited by

• Learning the Standard Model Manifold: Bayesian Latent Diffusion for Collider Anomaly Detection
• Dissecting Jet-Tagger Through Mechanistic Interpretability
• On the impact of selected modern deep-learning techniques to the performance and celerity of classification models in an experimental high-energy physics use case
• Sensing Higgs boson cascade decays through memory
• Identification of patterns in cosmic-ray arrival directions using dynamic graph convolutional neural networks
• Casting a graph net to catch dark showers
• Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed
• Machine Learning is Good for Physics - and Vice Versa
• Reconstructing the kinematics of deep inelastic scattering with deep learning

Related

• Machine learning in top quark physics at ATLAS and CMS
• Boosted Top Tagging Method Overview
• Deep-learning Top Taggers or The End of QCD?
• ML-Based Top Taggers: Performance, Uncertainty and Impact of Tower & Tracker Data Integration
• Playing Tag with ANN: Boosted Top Identification with Pattern Recognition