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

TL;DR

This paper applies machine learning, specifically neural networks, to classify orientifold properties of Calabi-Yau hypersurfaces in string theory, demonstrating high accuracy and potential for predicting properties beyond existing data.

Contribution

It introduces a neural network approach to identify orientifold Calabi-Yau properties from polytope data, revealing that orientifold symmetry may be encoded in the polytope structure.

Findings

Neural networks achieve high accuracy in classifying orientifold properties.

The approach can predict properties for higher-dimensional cases beyond the training data.

The orientifold symmetry appears to be encoded in the polytope structure.

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