Machine Learning Calabi-Yau Hypersurfaces
High Energy Physics - Theory
2022-03-23 v2 Algebraic Geometry
Machine Learning
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.
Cite
@article{arxiv.2112.06350,
title = {Machine Learning Calabi-Yau Hypersurfaces},
author = {David S. Berman and Yang-Hui He and Edward Hirst},
journal= {arXiv preprint arXiv:2112.06350},
year = {2022}
}
Comments
32 pages, 43 figures