The world in a grain of sand: Condensing the string vacuum degeneracy

Abstract

The proliferation of vacuum solutions to string theory highly deter the search for the Standard Model. We propose a novel approach to this problem by finding an efficient measure of similarity of vacua. Using one million concrete examples, the paradigm of few-shot machine-learning represents them as points in Euclidean three-space with similar points clustered together. We thereby compress the search space for desired physics to within one percent of the original. Our analysis provides an explicit method for machine-learning the landscape and finding ‘typicality’, even with minuscule data.

Keywords

• String theory
• Machine learning
• Neural networks
• String phenomenology
• Calabi-Yau compactifications