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20212025
most citedUniversal scaling limits of the symplectic elliptic Ginibre ensemble

18 citations · 72 across the 6 of their papers we have counts for

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6 papers

math.PR2025

Universal scaling limits at the spectral singularity of structured random matrices

Markus Ebke, Torben Krüger

The empirical spectral distribution of Hermitian -block random matrices converges to a deterministic density on the real line with a potential atom at the origin as the…

math-ph2023★ 13 cited

Universality in the number variance and counting statistics of the real and symplectic Ginibre ensemble

Gernot Akemann, Sung-Soo Byun, Markus Ebke +1

In this article, we compute and compare the statistics of the number of eigenvalues in a centred disc of radius in all three Ginibre ensembles. We determine the mean and varian…

math-ph2022★ 15 cited

Universality of the number variance in rotational invariant two-dimensional Coulomb gases

Gernot Akemann, Sung-Soo Byun, Markus Ebke

An exact map was established by Lacroix-A-Chez-Toine, Majumdar, and Schehr in [44] between the complex eigenvalues of complex non-Hermitian random matrices from the Ginibre ens…

math.PR2021★ 9 cited

Wronskian structures of planar symplectic ensembles

Sung-Soo Byun, Markus Ebke, Seong-Mi Seo

We consider the eigenvalues of non-Hermitian random matrices in the symmetry class of the symplectic Ginibre ensemble, which are known to form a Pfaffian point process in the plane…

math.PR2021★ 18 cited

Universal scaling limits of the symplectic elliptic Ginibre ensemble

Sung-Soo Byun, Markus Ebke

We consider the eigenvalues of symplectic elliptic Ginibre matrices which are known to form a Pfaffian point process whose correlation kernel can be expressed in terms of the skew-…

math-ph2021★ 17 cited

Skew-orthogonal polynomials in the complex plane and their Bergman-like kernels

Gernot Akemann, Markus Ebke, Iván Parra

Non-Hermitian random matrices with symplectic symmetry provide examples for Pfaffian point processes in the complex plane. These point processes are characterised by a matrix value…