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: | , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
29-01-2024
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| 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 |
| Zusammenfassung: | 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 simulation paradigm and extract easily interpretable formulas from complex simulated data. We introduce the method using the effect of a dimension-6 coefficient on associated ZH production. We then validate it for the known case of CP-violation in weak-boson-fusion Higgs production, including detector effects. |
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| Beschreibung: | Gesehen am 07.06.2024 |
| Beschreibung: | Online Resource |
| ISSN: | 2542-4653 |
| DOI: | 10.21468/SciPostPhys.16.1.037 |