Discovering the underlying analytic structure within Standard Model constants using artificial intelligence
This provides potential building blocks for model builders and AI methods to uncover hidden patterns in Standard Model constants, though it is incremental as it focuses on pairwise relationships rather than a comprehensive law.
The paper searched for underlying analytic relationships among Standard Model fundamental parameters using symbolic regression and genetic programming, identifying several simple expressions connecting pairs of constants with relative precision better than 1%.
This paper presents a search for underlying analytic structures among the fundamental parameters of the Standard Model (SM) using symbolic regression and genetic programming. 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.