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Back to the formula - LHC edition

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While neural networks offer an attractive way to numerically encode functions, actual formulas remain the language of theoretical particle physics. We use symbolic regression trained on matrix-element information to extract, for instance, optimal LHC observables. This way we invert the usual simulat...

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Main Authors: Butter, Anja (Author) , Plehn, Tilman (Author) , Soybelman, Nathalie (Author) , Brehmer, Johann (Author)
Format: Article (Journal)
Language:English
Published: 29-01-2024
In: SciPost physics
Year: 2024, Volume: 16, Issue: 1, Pages: 1-28
ISSN:2542-4653
DOI:10.21468/SciPostPhys.16.1.037
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.21468/SciPostPhys.16.1.037
Verlag, lizenzpflichtig, Volltext: https://scipost.org/10.21468/SciPostPhys.16.1.037
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Author Notes:Anja Butter, Tilman Plehn, Nathalie Soybelman and Johann Brehmer
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Back to the formula: LHC edition by Butter, Anja (Author) , Plehn, Tilman (Author) , Soybelman, Nathalie (Author) , Brehmer, Johann (Author) ,


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