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Signed distributions of real tensor eigenvectors of Gaussian tensor model via a four-Fermi theory. (English) Zbl 1518.81083

Summary: 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.

MSC:

81T32 Matrix models and tensor models for quantum field theory
60G15 Gaussian processes
60E05 Probability distributions: general theory
15A18 Eigenvalues, singular values, and eigenvectors
33C15 Confluent hypergeometric functions, Whittaker functions, \({}_1F_1\)
65C05 Monte Carlo methods

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