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Back to the Formula -- LHC Edition
by Anja Butter, Tilman Plehn, Nathalie Soybelman, Johann Brehmer
This Submission thread is now published as
Submission summary
| Authors (as registered SciPost users): | Tilman Plehn · Nathalie Soybelman |
| Submission information | |
|---|---|
| Preprint Link: | https://arxiv.org/abs/2109.10414v3 (pdf) |
| Date accepted: | Jan. 10, 2024 |
| Date submitted: | Feb. 10, 2023, 7:46 a.m. |
| Submitted by: | Tilman Plehn |
| Submitted to: | SciPost Physics |
| Ontological classification | |
|---|---|
| Academic field: | Physics |
| Specialties: |
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| Approach: | Phenomenological |
Abstract
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.
List of changes
All comments by both referees now incorporated, see individual referee responses.
Published as SciPost Phys. 16, 037 (2024)
Anonymous on 2023-08-17 [id 3910]
The authors have adequately addressed all previous notes and concerns, and the manuscript seems ready to be published. I have no further requests.