用人工智能发现标准模型常数间的隐藏数学关系
Discovering the Underlying Analytic Structure Within Standard Model Constants Using Artificial Intelligence
- 通过符号回归与遗传编程寻找常数间解析关系
- 发现多个相对精度优于1%的简洁表达式
- 为物理模型构建提供线索，适合理论物理与AI交叉研究者
本文提出一种方法，利用符号回归与遗传编程揭示标准模型（SM）基本参数间的隐藏解析关系。标准模型是描述基本粒子及其相互作用的基石理论。通过该方法，我们识别出连接这些常数对的最简解析关系，并报告了多个相对精度优于1%的显著表达式。这些结果可作为模型构建者及人工智能方法探索SM常数隐藏模式的宝贵输入，或可能作为构建统一理论的基础，用少数基本常数关联所有SM参数。
原文摘要 · Abstract (English)
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.
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