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Machine Learning in Top Physics in the ATLAS and CMS Collaborations

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arxiv 2301.09534 v1 pith:232T2Y4O submitted 2023-01-23 hep-ex

Philip Keicher This is my paper · ORCID
classification hep-ex
keywords collaborationsatlasapplicationslearningmachinephysicsaimsallowed
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 1 Pith paper

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  1. Transformer networks for Heavy flavor jet tagging

    hep-ph 2024-11 conditional novelty 2.0 of 10

    A review of transformer-based jet tagging that highlights the authors' CA-Mixer network as a state-of-the-art, faster alternative to Particle Transformer.

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