Signed distributions of real tensor eigenvectors of Gaussian tensor model via a four-fermi theory

Abstract

Eigenvalue distributions are important dynamical quantities in matrix models, and it is a challenging problem to derive them in tensor models. In this paper, we consider real symmetric order-three tensors with Gaussian distributions as the simplest case, and derive an explicit formula for signed distributions of real tensor eigenvectors: Each real tensor eigenvector contributes to the distribution by $\pm 1$, depending on the sign of the determinant of an associated Hessian matrix. The formula is expressed by the confluent hypergeometric function of the second kind, which is obtained by computing a partition function of a four-fermi theory. The formula can also serve as lower bounds of real eigenvector distributions (with no signs), and their tightness/looseness are discussed by comparing with Monte Carlo simulations. Large-$N$ limits are taken with the characteristic oscillatory behavior of the formula being preserved.

11 pages, 2 figures. Paragraphs added in the introduction, and related references added

References in corpus (1)

• Melonic dominance and the largest eigenvalue of a large random tensor

Cited by in corpus (1)

• Exact analytic expressions of real tensor eigenvalue distributions of Gaussian tensor model for small $N$