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

Understand

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 $h^{1,1}(X) \leq 6$ 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 $h^{1,1}(X)$.