The Fundamental Limit of Jet Tagging

TL;DR

This paper investigates the ultimate limits of jet tagging in particle physics using generative models to create a benchmark dataset, revealing a performance gap between current classifiers and the theoretical optimum.

Contribution

It introduces a realistic synthetic dataset with a known optimal jet tagging performance, enabling assessment of how close current models are to the fundamental limit.

Findings

Current classifiers lag behind the theoretical optimum.

A new benchmark dataset for jet tagging is provided.

Performance gap suggests room for future improvements.

Abstract

Identifying the origin of high-energy hadronic jets ('jet tagging') has been a critical benchmark problem for machine learning in particle physics. Jets are ubiquitous at colliders and are complex objects that serve as prototypical examples of collections of particles to be categorized. Over the last decade, machine learning-based classifiers have replaced classical observables as the state of the art in jet tagging. Increasingly complex machine learning models are leading to increasingly more effective tagger performance. Our goal is to address the question of convergence -- are we getting close to the fundamental limit on jet tagging or is there still potential for computational, statistical, and physical insights for further improvements? We address this question using state-of-the-art generative models to create a realistic, synthetic dataset with a known jet tagging optimum. Various state-of-the-art taggers are deployed on this dataset, showing that there is a significant gap between their performance and the optimum. Our dataset and software are made public to provide a benchmark task for future developments in jet tagging and other areas of particle physics.