Machine learning in top quark physics at ATLAS and CMS
High Energy Physics - Experiment
2026-02-04 v2
Abstract
This note presents an overview of current and potential future applications of machine-learning-based techniques in the study of the top quark. The research community has developed a diverse set of ideas and tools, including algorithms for the efficient reconstruction of recorded collision events and innovative methods for statistical inference. Recent applications of some techniques by the ATLAS and CMS collaborations are also highlighted.
Keywords
Cite
@article{arxiv.2503.04289,
title = {Machine learning in top quark physics at ATLAS and CMS},
author = {Matthias Komm},
journal= {arXiv preprint arXiv:2503.04289},
year = {2026}
}
Comments
Talk at the 17th International Workshop on Top Quark Physics (Top2024), 22-27 September 2024