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Back to the Formula -- LHC Edition

by Anja Butter, Tilman Plehn, Nathalie Soybelman, Johann Brehmer

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Submission summary

Authors (as registered SciPost users): Tilman Plehn · Nathalie Soybelman
Submission information
Preprint Link:  (pdf)
Date accepted: 2024-01-10
Date submitted: 2023-02-10 07:46
Submitted by: Plehn, Tilman
Submitted to: SciPost Physics
Ontological classification
Academic field: Physics
  • High-Energy Physics - Phenomenology
Approach: Phenomenological


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)

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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.