Accuracy versus precision in boosted top tagging with the ATLAS detector

TL;DR

This paper evaluates machine learning top tagging algorithms at ATLAS, highlighting the trade-off between accuracy and systematic uncertainties, and provides datasets for further research.

Contribution

It systematically studies the systematic uncertainties of ML-based top tagging algorithms using ATLAS data and makes the datasets publicly available.

Findings

Most performant algorithms have the largest uncertainties.

Systematic uncertainties are significant and motivate development of improved methods.

Datasets are made publicly available for community use.

Abstract

The identification of top quark decays where the top quark has a large momentum transverse to the beam axis, known as $top$ $tagging$, is a crucial component in many measurements of Standard Model processes and searches for beyond the Standard Model physics at the Large Hadron Collider. Machine learning techniques have improved the performance of top tagging algorithms, but the size of the systematic uncertainties for all proposed algorithms has not been systematically studied. This paper presents the performance of several machine learning based top tagging algorithms on a dataset constructed from simulated proton-proton collision events measured with the ATLAS detector at $\sqrt{s} = 13$ TeV. The systematic uncertainties associated with these algorithms are estimated through an approximate procedure that is not meant to be used in a physics analysis, but is appropriate for the level of precision required for this study. The most performant algorithms are found to have the largest uncertainties, motivating the development of methods to reduce these uncertainties without compromising performance. To enable such efforts in the wider scientific community, the datasets used in this paper are made publicly available.