Machine Learning in Top Physics in the ATLAS and CMS Collaborations

2023/01/23 by Philip Keicher, Keicher, Philip · 1 citation
Computer Science · Physics and Astronomy · #Distributed and Parallel Computing Systems #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #Particle Detector Development and Performance #Particle physics theoretical and experimental studies

paper · pdf · doi:10.48550/arxiv.2301.09534

openalex publication_date 2023/01/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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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