Symbolic regression for precision LHC physics

TL;DR

This paper explores the use of symbolic regression to derive compact, accurate analytic expressions for high energy physics phenomena, validated on QED and applied to Drell-Yan processes at the LHC.

Contribution

It demonstrates the potential of symbolic regression to produce reliable analytic formulas in particle physics, bridging machine learning and phenomenological analysis.

Findings

SR successfully recovers known QED equations

SR provides promising approximations for Drell-Yan structure functions

Method enhances interpretability and accuracy of phenomenological models

Abstract

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.