The proliferation of vacuum solutions to string theory highly deter the search for the Standard Model within the string landscape. We propose a novel approach to this problem by using machine learning to find a measure of similarity of vacua. Using complete intersection Calabi Yau manifolds as a concrete example, our approach represents these manifolds as points in Euclidean three-space with similar points clustered together. Our analysis provides an explicit method for machine-learning the landscape even with minuscule data.
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