Machine Learning Calabi-Yau Hypersurfaces

12/12/2021

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by   David S. Berman, et al.

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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 learning also allows us to identify weighted-P4s which admit Calabi-Yau hypersurfaces to 100 clustering behaviour.