Machine-Learning Number Fields

Abstract

We show that standard machine-learning algorithms may be trained to predict certain invariants of algebraic number fields to high accuracy. A random-forest classifier that is trained on finitely many Dedekind zeta coefficients is able to distinguish between real quadratic fields with class number 1 and 2, to 0.96 precision. Furthermore, the classifier is able to extrapolate to fields with discriminant outside the range of the training data. When trained on the coefficients of defining polynomials for Galois extensions of degrees 2, 6, and 8, a logistic regression classifier can distinguish between Galois groups and predict the ranks of unit groups with precision >0.97.

arXiv comment20 pages, 1 figure, 3 tables

References in corpus (6)

• Machine Learning of Calabi-Yau Volumes
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• Machine Learning meets Number Theory: The Data Science of Birch-Swinnerton-Dyer
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