Machine learning invariants of arithmetic curves

He, Y.-H., Lee, K.-H. and Oliver, T. 2023. Machine learning invariants of arithmetic curves. Journal of Symbolic Computation. 115, pp. 478-491. https://doi.org/10.1016/j.jsc.2022.08.017

TitleMachine learning invariants of arithmetic curves
TypeJournal article
AuthorsHe, Y.-H., Lee, K.-H. and Oliver, T.
Abstract

We show that standard machine learning algorithms may be trained to predict certain invariants of low genus arithmetic curves. Using datasets of size around 105, we demonstrate the utility of machine learning in classification problems pertaining to the BSD invariants of an elliptic curve (including its rank and torsion subgroup), and the analogous invariants of a genus 2 curve. Our results show that a trained machine can efficiently classify curves according to these invariants with high accuracies (>0.97). For problems such as distinguishing between torsion orders, and the recognition of integral points, the accuracies can reach 0.998.

KeywordsMachine-learning
Arithmetic geometry
Elliptic curves
Hyper-elliptic curves
Birch-Swinnerton-Dyer conjecture
JournalJournal of Symbolic Computation
Journal citation115, pp. 478-491
ISSN1095-855X
0747-7171
Year2023
PublisherElsevier
Publisher's version
License
CC BY 4.0
File Access Level
Open (open metadata and files)
Digital Object Identifier (DOI)https://doi.org/10.1016/j.jsc.2022.08.017
Web address (URL)https://www.sciencedirect.com/science/article/pii/S0747717122000839
Publication dates
PublishedMar 2023
Published online22 Aug 2022

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