$\mathcal{CP}$-Analyses with Symbolic Regression

TL;DR

This paper introduces a symbolic regression approach to discover interpretable $ ext{CP}$-sensitive observables in Higgs boson interactions at the LHC, offering a balance of interpretability and performance over traditional machine learning methods.

Contribution

The paper presents a novel application of symbolic regression to learn analytic $ ext{CP}$-sensitive formulas for Higgs interactions, improving interpretability and performance.

Findings

Learned $ ext{CP}$-sensitive observables at detector level.

Symbolic regression provides interpretable formulas.

Improved performance over traditional ML methods.

Abstract

Searching for $\mathcal{CP}$ violation in Higgs interactions at the LHC is as challenging as it is important. Although modern machine learning outperforms traditional methods, its results are difficult to control and interpret, which is especially important if an unambiguous probe of a fundamental symmetry is required. We propose solving this problem by learning analytic formulas with symbolic regression. Using the complementary PySR and SymbolNet approaches, we learn $\mathcal{CP}$-sensitive observables at the detector level for WBF Higgs production and top-associated Higgs production. We find that they offer advantages in interpretability and performance.