2016
DOI: 10.1088/0951-7715/29/11/3385
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Gaussian perturbations of hard edge random matrix ensembles

Abstract: We study the eigenvalue correlations of random Hermitian n × n matrices of the form S = M + ǫH, where H is a GUE matrix, ǫ > 0, and M is a positive-definite Hermitian random matrix, independent of H, whose eigenvalue density is a polynomial ensemble. We show that there is a soft-to-hard edge transition in the microscopic behaviour of the eigenvalues of S close to 0 if ǫ tends to 0 together with n → +∞ at a critical speed, depending on the random matrix M . In a double scaling limit, we obtain a new family of l… Show more

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Cited by 3 publications
(4 citation statements)
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“…A similar hard edge phase transition occurs in three different regimes for the shifted mean chiral Gaussian ensemble [28]. Recently, some different types of hard-to-soft edge transition have been observed for Gaussian perturbations of hard edge random matrix ensembles by Claeys and Doeraene [16]. Also, see [10] for the famous Baik-Ben Arous-Péché phase transition for largest eigenvalues.…”
Section: 2mentioning
confidence: 70%
“…A similar hard edge phase transition occurs in three different regimes for the shifted mean chiral Gaussian ensemble [28]. Recently, some different types of hard-to-soft edge transition have been observed for Gaussian perturbations of hard edge random matrix ensembles by Claeys and Doeraene [16]. Also, see [10] for the famous Baik-Ben Arous-Péché phase transition for largest eigenvalues.…”
Section: 2mentioning
confidence: 70%
“…The first case is for a general Pólya ensemble without a shift on either the Hermitian antisymmetric matrices H 1 , the Hermitian matrices H 2 , the Hermitian anti-self-dual matrices H 4 or the complex rectangular matrices M ν . The other two cases we considered are the eigenvalue/squared singular value statistics of the Pólya ensemble added by a either a fixed matrix or a random matrix drawn from a polynomial ensemble on the same space as the Pólya ensemble, see [4,14,38,[41][42][43][44]. All results hold for finite matrix dimension.…”
Section: Discussionmentioning
confidence: 99%
“…This result was already derived in [15]. This transformation formula was applied in [14] where the polynomial ensemble was chosen to be the Laguerre ensemble, products of Ginibre matrices or matrices drawn from the Jacobi ensemble and Muttalib-Borodin ensembles.…”
Section: Corollary Iii9 (Simplification Of Theorem Iii8)mentioning
confidence: 99%
“…For the correlation kernels we arrive at the transformation formula which could be useful for asymptotic analysis. A similar formula for the case of a sum with a GUE matrix was given in [11] and it was used for asymptotic analysis in [9] and [10].…”
Section: By Linearity We Havementioning
confidence: 99%