Symbolic regression for precision LHC physics

Autor:	Morales-Alvarado, Manuel, Conde, Daniel, Bendavid, Josh, Sanz, Veronica, Ubiali, Maria
Rok vydání:	2024
Předmět:	High Energy Physics - Phenomenology
Druh dokumentu:	Working Paper
Popis:	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. Comment: 7 pages, 7 figures, 3 tables. Accepted for the Machine Learning and the Physical Sciences Workshop NeurIPS 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.07839 Zobrazit plný text záznamu View this record from Arxiv