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arXiv · 2112.06350

Machine Learning Calabi-Yau Hypersurfaces

David S. Berman·Yang-Hui He·Edward Hirst
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Abstract

We revisit the classic database of weighted-P4s which admit Calabi-Yau 3-fold hypersurfaces equipped with a diverse set of tools from the machine-learning toolbox. Unsupervised techniques identify an unanticipated almost linear dependence of the topological data on the weights. This then allows us to identify a previously unnoticed clustering in the Calabi-Yau data. Supervised techniques are successful in predicting the topological parameters of the hypersurface from its weights with an accuracy of R^2 > 95%. Supervised learning also allows us to identify weighted-P4s which admit Calabi-Yau hypersurfaces to 100% accuracy by making use of partitioning supported by the clustering behaviour.

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David S. Berman, Yang-Hui He, Edward Hirst. 2021-12-12. Machine Learning Calabi-Yau Hypersurfaces. https://doi.org/10.1103/physrevd.105.066002

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