Back to the Formula -- LHC Edition

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.

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Cited by in corpus (7)

• An implementation of neural simulation-based inference for parameter estimation in ATLAS
• SymbolFit: Automatic Parametric Modeling with Symbolic Regression
• Machine Learning-Based Analytical Expressions for Gray-Body Factors and Application to Primordial Black Holes
• A rotation-equivariant graph neural network for learning hadronic SMEFT effects
• Accelerating Graph-based Tracking Tasks with Symbolic Regression
• Equivariant neural networks for robust $\text{CP}$ observables
• The Physics Behind ML-based Quark-Gluon Taggers