New Calabi-Yau Manifolds from Genetic Algorithms
arXiv:2306.06159 · doi:10.1016/j.physletb.2024.138504
Abstract
Calabi-Yau manifolds can be obtained as hypersurfaces in toric varieties built from reflexive polytopes. We generate reflexive polytopes in various dimensions using a genetic algorithm. As a proof of principle, we demonstrate that our algorithm reproduces the full set of reflexive polytopes in two and three dimensions, and in four dimensions with a small number of vertices and points. Motivated by this result, we construct five-dimensional reflexive polytopes with the lowest number of vertices and points. By calculating the normal form of the polytopes, we establish that many of these are not in existing datasets and therefore give rise to new Calabi-Yau four-folds. In some instances, the Hodge numbers we compute are new as well.
24 pages, 2 figures. Accepted version
References in corpus (3)
Cited by in corpus (8)
- Hamiltonian Neural Networks approach to fuzzball geodesics
- Generative AI for Brane Configurations and Coamoeba
- Interpretable and physics-informed emulator for the linear matter power spectrum from machine learning
- Calabi-Yau Four/Five/Six-folds as Hypersurfaces: Machine Learning, Approximation, and Generation
- Machine Learning Clifford invariants of ADE Coxeter elements
- evortran: a modern Fortran package for genetic algorithms with applications from LHC data fitting to LISA signal reconstruction
- Machine Learning Mutation-Acyclicity of Quivers
- Learning 3-Manifold Triangulations