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Machine Learning Calabi-Yau Hypersurfaces

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arxiv 2112.06350 v2 pith:CEKI5S3Q submitted 2021-12-12 hep-th math.AGstat.ML

David S. Berman , Yang-Hui He , Edward Hirst This is my paper
classification hep-thmath.AGstat.ML
keywords calabi-yauhypersurfacesidentifyaccuracyadmitallowsclusteringdata
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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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