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 show how symbolic regression trained on matrix-element information provides, for instance, optimal LHC observables in an easily interpretable form. W...
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| Hauptverfasser: | , , , |
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| Dokumenttyp: | Article (Journal) Kapitel/Artikel |
| Sprache: | Englisch |
| Veröffentlicht: |
15 Nov 2021
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| In: |
Arxiv
Year: 2021, Pages: 1-29 |
| DOI: | 10.48550/arXiv.2109.10414 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.48550/arXiv.2109.10414 Verlag, lizenzpflichtig, Volltext: http://arxiv.org/abs/2109.10414 
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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 show how symbolic regression trained on matrix-element information provides, for instance, optimal LHC observables in an easily interpretable form. 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 14.09.2022 |
| Beschreibung: | Online Resource |
| DOI: | 10.48550/arXiv.2109.10414 |