Distinguishing Elliptic Fibrations with AI

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.

6 pages, 1 table, 4 figures; v2: four-fold learning vastly improved in section III.C, four-fold statistics fixed in table I, comments added on dataset enhancement

References in corpus (2)

• Machine Learning of Calabi-Yau Volumes
• Emergent Strings, Duality and Weak Coupling Limits for Two-Form Fields

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