Discovering the Underlying Analytic Structure Within Standard Model Constants Using Artificial Intelligence

TL;DR

This paper uses AI-driven symbolic regression to discover simple, precise analytic relationships among Standard Model constants, potentially revealing underlying physical laws.

Contribution

It introduces a novel AI-based method for uncovering hidden analytic relationships among fundamental physics constants using symbolic regression.

Findings

Identified several analytic expressions with better than 1% precision.

Discovered relationships that could serve as building blocks for a deeper theory.

Provided a framework for future AI-driven exploration of fundamental constants.

Abstract

This paper presents a method for uncovering hidden analytic relationships among the fundamental parameters of the Standard Model (SM), a foundational theory in physics that describes the fundamental particles and their interactions, using symbolic regression and genetic programming. Using this approach, we identify the simplest analytic relationships connecting pairs of these constants and report several notable expressions obtained with relative precision better than 1%. These results may serve as valuable inputs for model builders and artificial intelligence methods aimed at uncovering hidden patterns among the SM constants, or potentially used as building blocks for a deeper underlying law that connects all parameters of the SM through a small set of fundamental constants.