  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.
