Probing the Structure of String Theory Vacua with Genetic Algorithms and Reinforcement Learning

TL;DR

This paper explores the use of genetic algorithms and reinforcement learning to efficiently search the high-dimensional string landscape for vacua with specific physical properties, revealing new symmetries and features.

Contribution

It introduces a novel approach combining machine learning techniques to identify and analyze string theory vacua, uncovering previously unknown symmetries in the solutions.

Findings

Revealed new symmetries in string vacua

Demonstrated effectiveness of ML methods in high-dimensional searches

Reduced sampling bias by combining search techniques

Abstract

Identifying string theory vacua with desired physical properties at low energies requires searching through high-dimensional solution spaces - collectively referred to as the string landscape. We highlight that this search problem is amenable to reinforcement learning and genetic algorithms. In the context of flux vacua, we are able to reveal novel features (suggesting previously unidentified symmetries) in the string theory solutions required for properties such as the string coupling. In order to identify these features robustly, we combine results from both search methods, which we argue is imperative for reducing sampling bias.