Vacuum Selection from Cosmology on Networks of String Geometries.

Area of Science:

• Theoretical Physics
• String Theory
• Network Science

Background:

• The string landscape is vast and complex, posing challenges for theoretical physics.
• Understanding vacuum selection is crucial for connecting string theory to observable cosmology.

Purpose of the Study:

• To apply network science as a novel framework for investigating the string landscape.
• To identify mechanisms for vacuum selection within string theory models.

Main Methods:

• Construction of two large networks representing string geometries.
• Nodes represent extra-dimensional six-manifolds; edges represent topological transitions.
• Analysis of a bubble cosmology model using network properties.

Main Results:

• The late-time behavior of the cosmology model is determined by the largest eigenvector of -(L+D).
• L and D represent the Laplacian and degree matrices of the constructed networks.
• This provides a dynamical mechanism for vacuum selection.

Conclusions:

• Network science provides a powerful tool for studying the string landscape.
• The eigenvector analysis offers a concrete mechanism for vacuum selection in string cosmology.