Symbolic regression for beyond the standard model physics

Abdussalam, Shehu; Abel, Steven; Romão, Miguel Crispim

Abstract

We propose symbolic regression as a powerful tool for the numerical studies of proposed models of physics beyond the Standard Model. In this paper we demonstrate the efficacy of the method on a benchmark model, namely the constrained minimal supersymmetric Standard Model, which has a four-dimensional parameter space. We provide a set of analytical expressions that reproduce three low-energy observables of interest in terms of the parameters of the theory: the Higgs mass, the contribution to the anomalous magnetic moment of the muon, and the cold dark matter relic density. To demonstrate the power of the approach, we employ the symbolic expressions in a global fits analysis to derive the posterior probability densities of the parameters, which are obtained two orders of magnitude more rapidly than is possible using conventional methods.

Citation

Abdussalam, S., Abel, S., & Romão, M. C. (2025). Symbolic regression for beyond the standard model physics. Physical Review D, 111(1), Article 015022. https://doi.org/10.1103/PhysRevD.111.015022

• Journal Article Type: Article
• Acceptance Date: Jan 7, 2025
• Online Publication Date: Jan 23, 2025
• Publication Date: Jan 1, 2025
• Deposit Date: Apr 7, 2025
• Publicly Available Date: Apr 7, 2025
• Journal: Physical Review D
• Print ISSN: 2470-0010
• Electronic ISSN: 2470-0029
• Publisher: American Physical Society
• Peer Reviewed: Peer Reviewed
• Volume: 111
• Issue: 1
• Article Number: 015022
• DOI: https://doi.org/10.1103/PhysRevD.111.015022
• Public URL: https://durham-repository.worktribe.com/output/3782329