Applying machine learning to the Calabi-Yau orientifolds with string vacua

Abstract

We use the machine learning technique to search the polytope which can result in an orientifold Calabi-Yau hypersurface and the "naive Type IIB string vacua". We show that neural networks can be trained to give a high accuracy for classifying the orientifold property and vacua based on the newly generated orientifold Calabi-Yau database with $h^{1,1}(X)\le 6$ arXiv:2111.03078. This indicates the orientifold symmetry may already be encoded in the polytope structure. In the end, we try to use the trained neural networks model to go beyond the database and predict the orientifold signal of polytope for higher $h^{1,1}(X)$.

27 pages, 4 figures, 3 tables

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• Machine Learning on generalized Complete Intersection Calabi-Yau Manifolds
• Calabi-Yau Four/Five/Six-folds as ${\mathbb{P}}_{\text{w}}^n$ Hypersurfaces: Machine Learning, Approximation, and Generation
• Divisor topologies of CICY 3-folds and their applications to phenomenology
• A Database of Calabi-Yau Orientifolds and the Size of D3-Tadpoles