Getting CICY High

Abstract

Supervised machine learning can be used to predict properties of string geometries with previously unknown features. Using the complete intersection Calabi-Yau (CICY) threefold dataset as a theoretical laboratory for this investigation, we use low $h^{1,1}$ geometries for training and validate on geometries with large $h^{1,1}$. Neural networks and Support Vector Machines successfully predict trends in the number of Kähler parameters of CICY threefolds. The numerical accuracy of machine learning improves upon seeding the training set with a small number of samples at higher $h^{1,1}$.

18 pages, 3 figures

References in corpus (9)

• Heterotic Compactification, An Algorithmic Approach
• Machine Learning of Calabi-Yau Volumes
• Machine Learning in the String Landscape
• A Calabi-Yau Database: Threefolds Constructed from the Kreuzer-Skarke List
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• Fibrations in CICY Threefolds
• Genetic Algorithms and the Search for Viable String Vacua
• A universal order parameter for Inverse Magnetic Catalysis
• Hodge Numbers for All CICY Quotients

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• Machine Learning Calabi-Yau Hypersurfaces
• Machine Learning Lie Structures & Applications to Physics
• Contrast data mining for the MSSM from strings
• Machine Learning meets Number Theory: The Data Science of Birch-Swinnerton-Dyer
• Breeding realistic D-brane models
• Ising Machines for Diophantine Problems in Physics
• Deep learning complete intersection Calabi-Yau manifolds