Inflation with a graceful exit in a random landscape

Abstract

We develop a stochastic description of small-field inflationary histories with a graceful exit in a random potential whose Hessian is a Gaussian random matrix as a model of the unstructured part of the string landscape. The dynamical evolution in such a random potential from a small-field inflation region towards a viable late-time de Sitter (dS) minimum maps to the dynamics of Dyson Brownian motion describing the relaxation of non-equilibrium eigenvalue spectra in random matrix theory. We analytically compute the relaxation probability in a saddle point approximation of the partition function of the eigenvalue distribution of the Wigner ensemble describing the mass matrices of the critical points. When applied to small-field inflation in the landscape, this leads to an exponentially strong bias against small-field ranges and an upper bound $N\ll 10$ on the number of light fields $N$ participating during inflation from the non-observation of negative spatial curvature.

Published version

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• Seven Lessons from Manyfield Inflation in Random Potentials
• Initial conditions for slow-roll inflation in a random Gaussian landscape
• Inflation in multi-field random Gaussian landscapes
• Towards a complete mass spectrum of type-IIB flux vacua at large complex structure
• Learning to Inflate
• Inflation in Random Landscapes with two energy scales
• Slepian models for Gaussian Random Landscapes
• Efficient circular Dyson Brownian motion algorithm