City Research Online

Distinguishing elliptic fibrations with AI

He, Y. ORCID: 0000-0002-0787-8380 and Lee, S-J. (2019). Distinguishing elliptic fibrations with AI. Physics Letters B, doi: 10.1016/j.physletb.2019.134889

Abstract

We use the latest techniques in machine-learning to study whether from the landscape of Calabi-Yau manifolds one can distinguish elliptically fibred ones. Using the dataset of complete intersections in products of projective spaces (CICY3 and CICY4, totalling about a million manifolds) as a concrete playground, we find that a relatively simple neural network with forward-feeding multi-layers can very efficiently distinguish the elliptic fibrations, much more so than using the traditional methods of manipulating the defining equations. We cross-check with control cases to ensure that the AI is not randomly guessing and is indeed identifying an inherent structure. Our result should prove useful in F-theory and string model building as well as in pure algebraic geometry.

Publication Type: Article
Additional Information: ©2019 Published by Elsevier B.V. This is an open access article under the CC-BY license (http://creativecommons.org/licenses/by/4.0/).
Subjects: Q Science > QA Mathematics
Q Science > QC Physics
Departments: School of Mathematics, Computer Science & Engineering > Mathematics
URI: http://openaccess.city.ac.uk/id/eprint/22747
[img]
Preview
Text - Published Version
Available under License Creative Commons Attribution.

Download (488kB) | Preview

Export

Downloads

Downloads per month over past year

View more statistics

Actions (login required)

Admin Login Admin Login