Journal of High Energy Physics (May 2020)

Slepian models for Gaussian random landscapes

  • Jose J. Blanco-Pillado,
  • Kepa Sousa,
  • Mikel A. Urkiola

DOI
https://doi.org/10.1007/JHEP05(2020)142
Journal volume & issue
Vol. 2020, no. 5
pp. 1 – 47

Abstract

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Abstract Phenomenologically interesting scalar potentials are highly atypical in generic random landscapes. We develop the mathematical techniques to generate constrained random potentials, i.e. Slepian models, which can globally represent low-probability realizations of the landscape. We give analytical as well as numerical methods to construct these Slepian models for constrained realizations of a full Gaussian random field around critical as well as inflection points. We use these techniques to numerically generate in an efficient way a large number of minima at arbitrary heights of the potential and calculate their non-perturbative decay rate. Furthermore, we also illustrate how to use these methods by obtaining statistical information about the distribution of observables in an inflationary inflection point constructed within these models.

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