Machine Learning in Top Physics in the ATLAS and CMS Collaborations
Philip Keicher
TL;DR
This paper reviews the current and future applications of machine learning in top-quark physics within the ATLAS and CMS collaborations, highlighting its importance and ongoing research efforts.
Contribution
It provides a comprehensive overview of how machine learning techniques are applied and developed in top-quark physics in major collider experiments.
Findings
Machine learning is crucial for top-quark physics analysis.
Current applications include event classification and data analysis.
Ongoing studies aim to enhance future physics measurements.
Abstract
Machine learning is essential in many aspects of top-quark related physics in the ATLAS and CMS Collaborations. This work aims to give a brief overview over current applications in the two collaborations as well as on-going studies for future applications. Copyright 2023 CERN for the benefit of the ATLAS and CMS Collaborations. Reproduction of this article or parts of it is allowed as specified in the CC-BY-4.0 license
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Taxonomy
TopicsParticle physics theoretical and experimental studies · Particle Detector Development and Performance · Distributed and Parallel Computing Systems