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Machine learning Calabi-Yau hypersurfaces

Berman, D. S., He, Y-H. ORCID: 0000-0002-0787-8380 & Hirst, E. ORCID: 0000-0003-1699-4399 (2022). Machine learning Calabi-Yau hypersurfaces. Physical Review D, 105(6), doi: 10.1103/PhysRevD.105.066002

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 R2>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 behavior.

Publication Type: Article
Additional Information: Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP3.
Subjects: Q Science > QA Mathematics
Q Science > QC Physics
Departments: School of Mathematics, Computer Science & Engineering > Mathematics
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