Vacuum Selection from Cosmology on Networks of String Geometries

Abstract

We introduce network science as a framework for studying the string landscape. Two large networks of string geometries are constructed, where nodes are extra-dimensional six-manifolds and edges represent topological transitions between them. We show that a standard bubble cosmology model on the networks has late-time behavior determined by the largest eigenvector of $-(\mathbf{L}+\mathbf{D})$, where $\mathbf{L}$ and $\mathbf{D}$ are the Laplacian and degree matrices of the networks, which provides a dynamical mechanism for vacuum selection in the string landscape.

PRL version

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• AdS/CFT as a deep Boltzmann machine
• Machine learning Calabi-Yau metrics
• Deep learning in the heterotic orbifold landscape
• Computational Complexity of Vacua and Near-Vacua in Field and String Theory
• Getting CICY High
• The Calabi-Yau Landscape: from Geometry, to Physics, to Machine-Learning
• Distinguishing Elliptic Fibrations with AI
• The Self-Organized Critical Multiverse
• Estimating Calabi-Yau Hypersurface and Triangulation Counts with Equation Learners
• On Axion Reheating in the String Landscape
• Search Optimization, Funnel Topography, and Dynamical Criticality on the String Landscape
• Accessibility Measure for Eternal Inflation: Dynamical Criticality and Higgs Metastability
• Statistical Predictions in String Theory and Deep Generative Models
• The landscape of promising non-supersymmetric string models
• Kahler Moduli Stabilization and the Propagation of Decidability
• Early-Time Measure in Eternal Inflation
• Constraints on Standard Model Constructions in F-theory
• A Local Wheeler-DeWitt Measure for the String Landscape
• Universes as Big Data
• Topological Data Analysis for the String Landscape
• Lectures on Naturalness, String Landscape and Multiverse
• Probing Black Hole Microstate Evolution with Networks and Random Walks
• Chaotic D1-D5 Black Hole Dynamics through Networks