Complex symmetric, self-dual, and Ginibre random matrices: Analytical results for three classes of bulk and edge statistics
Gernot Akemann, Noah Ayg\"un, Mario Kieburg, Patricia P\"a{\ss}ler

TL;DR
This paper analytically compares three classes of non-Hermitian Gaussian random matrices, confirming they have distinct local spectral statistics at bulk and edges, supporting a recent universality conjecture.
Contribution
It derives explicit formulas for characteristic polynomial expectations in three matrix classes and analyzes their spectral statistics, revealing differences and similarities.
Findings
Distinct local bulk and edge spectral statistics for the three classes.
Explicit expressions for characteristic polynomial expectations for finite and infinite matrices.
Effective Lagrangians for the associated non-linear sigma models are derived, showing universality in their form.
Abstract
Recently, a conjecture about the local bulk statistics of complex eigenvalues has been made based on numerics. It claims that there are only three universality classes, which have all been observed in open chaotic quantum systems. Motivated by these new insights, we compute and compare the expectation values of pairs of complex conjugate characteristic polynomials in three ensembles of Gaussian non-Hermitian random matrices representative for the three classes: the well-known complex Ginibre ensemble, complex symmetric and complex self-dual matrices. In the Cartan classification scheme of non-Hermitian random matrices they are labelled as class A, AI and AII, respectively. Using the technique of Grassmann variables, we derive explicit expressions for a single pair of expected characteristic polynomials for finite as well as infinite matrix dimension. For the latter we…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRandom Matrices and Applications
