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.
@article{arxiv.2109.10414,
title = {Back to the Formula -- LHC Edition},
author = {Anja Butter and Tilman Plehn and Nathalie Soybelman and Johann Brehmer},
journal= {arXiv preprint arXiv:2109.10414},
year = {2024}
}