Discover the GellMann-Okubo formula with machine learning
High Energy Physics - Phenomenology
2022-10-26 v1
Abstract
Machine learning is a novel and powerful technology and has been widely used in various science topics. We demonstrate a machine-learning based approach built by a set of general metrics and rules inspired by physics. Taking advantages of physical constraints, such as dimension identity, symmetry and generalization, we succeed to rediscover the GellMann Okubo formula using a technique of symbolic regression. This approach can effectively find explicit solutions among user-defined observable, and easily extend to study on exotic hadron spectrum.
Cite
@article{arxiv.2208.03165,
title = {Discover the GellMann-Okubo formula with machine learning},
author = {Zhenyu Zhang and Rui Ma and Jifeng Hu and Qian Wang},
journal= {arXiv preprint arXiv:2208.03165},
year = {2022}
}
Comments
7 pages, 3 figures