Back to the Formula -- LHC Edition
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2021
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| _version_ | 1866909086497374208 |
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| author | Butter, Anja Plehn, Tilman Soybelman, Nathalie Brehmer, Johann |
| author_facet | Butter, Anja Plehn, Tilman Soybelman, Nathalie Brehmer, Johann |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2109_10414 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Back to the Formula -- LHC Edition Butter, Anja Plehn, Tilman Soybelman, Nathalie Brehmer, Johann High Energy Physics - Phenomenology 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. |
| title | Back to the Formula -- LHC Edition |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2109.10414 |