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Symbolic regression for precision LHC physics
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We study the potential of symbolic regression (SR) to derive compact and precise analytic expressions that can improve the accuracy and simplicity of phenomenological analyses at the Large Hadron Collider (LHC). As a benchmark, we apply SR to equation recovery in quantum electrodynamics (QED), where established analytical results from quantum field theory provide a reliable framework for evaluation. This benchmark serves to validate the performance and reliability of SR before extending its application to structure functions in the Drell-Yan process mediated by virtual photons, which lack analytic representations from first principles. By combining the simplicity of analytic expressions with the predictive power of machine learning techniques, SR offers a useful tool for facilitating phenomenological analyses in high energy physics.
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Cited by 2 Pith papers
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Symbolic Extraction of Non-Perturbative Transverse-Momentum-Dependent Distributions from Drell-Yan Data
Symbolic regression on a factorized NN fit to 482 Drell–Yan points yields a 9-constant analytical non-perturbative TMD with χ²/ndf≈1.04 and a retained x–b_T cross term.
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$\mathcal{CP}$-Analyses with Symbolic Regression
Symbolic regression produces analytic, detector-level CP-odd observables for WBF Higgs production and an analytic reconstruction of the Collins-Soper angle in ttH that are competitive with black-box ML and classical methods.
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