Machine Learning Class Numbers of Real Quadratic Fields
Number Theory
2025-01-14 v1 Machine Learning
High Energy Physics - Theory
Abstract
We implement and interpret various supervised learning experiments involving real quadratic fields with class numbers 1, 2 and 3. We quantify the relative difficulties in separating class numbers of matching/different parity from a data-scientific perspective, apply the methodology of feature analysis and principal component analysis, and use symbolic classification to develop machine-learned formulas for class numbers 1, 2 and 3 that apply to our dataset.
Cite
@article{arxiv.2209.09283,
title = {Machine Learning Class Numbers of Real Quadratic Fields},
author = {Malik Amir and Yang-Hui He and Kyu-Hwan Lee and Thomas Oliver and Eldar Sultanow},
journal= {arXiv preprint arXiv:2209.09283},
year = {2025}
}
Comments
26 pages, 20 figures