Back to the formula - LHC edition

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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Hauptverfasser: Butter, Anja (VerfasserIn) , Plehn, Tilman (VerfasserIn) , Soybelman, Nathalie (VerfasserIn) , Brehmer, Johann (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 29-01-2024
In: SciPost physics
Year: 2024, Jahrgang: 16, Heft: 1, Pages: 1-28
ISSN:2542-4653
DOI:10.21468/SciPostPhys.16.1.037
Online-Zugang: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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Verfasserangaben:Anja Butter, Tilman Plehn, Nathalie Soybelman and Johann Brehmer

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