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Riemannian Gaussian distributions were initially introduced as basic building blocks for learning models which aim to capture the intrinsic structure of statistical populations of positive-definite matrices (here called covariance…

Statistics Theory · Mathematics 2023-02-16 Salem Said , Simon Heuveline , Cyrus Mostajeran

As we move towards future galaxy surveys, the three-point statistics will be increasingly leveraged to enhance the constraining power of the data on cosmological parameters. An essential part of the three-point function estimation is…

Cosmology and Nongalactic Astrophysics · Physics 2019-05-22 David W. Pearson , Lado Samushia

We study the universality of spectral statistics of large random matrices. We consider $N\times N$ symmetric, hermitian or quaternion self-dual random matrices with independent, identically distributed entries (Wigner matrices) where the…

Mathematical Physics · Physics 2015-05-18 Laszlo Erdos

Sparse non-Hermitian random matrices arise in the study of disordered physical systems with asymmetric local interactions, and have applications ranging from neural networks to ecosystem dynamics. The spectral characteristics of these…

Statistical Mechanics · Physics 2024-02-21 Fernando Lucas Metz , Izaak Neri , Tim Rogers

Number theorists have studied extensively the connections between the distribution of zeros of the Riemann $\zeta$-function, and of some generalizations, with the statistics of the eigenvalues of large random matrices. It is interesting to…

Mathematical Physics · Physics 2009-10-31 E. Brezin , S. Hikami

We develop technique concerning statistical analyses of the large-scale structure of the universe in the presence of biasing in the structure formation. We formulate the diagrammatic method to calculate the correlation functions of the…

Astrophysics · Physics 2016-08-30 Takahiko Matsubara

We use a smoothed version of the explicit formula to find an approximation to the Riemann zeta function as a product over its nontrivial zeros multiplied by a product over the primes. We model the first product by characteristic polynomials…

Number Theory · Mathematics 2007-05-23 S. M. Gonek , C. P. Hughes , J. P. Keating

In this paper, we survey some recent progress on rigorously establishing the universality of various spectral statistics of Wigner Hermitian random matrix ensembles, focusing on the Four Moment Theorem and its refinements and applications,…

Probability · Mathematics 2012-02-02 Terence Tao , Van Vu

For general large non-Hermitian random matrices $X$ and deterministic normal deformations $A$, we prove that the local eigenvalue statistics of $A+X$ close to the critical edge points of its spectrum are universal. This concludes the proof…

Probability · Mathematics 2025-07-14 Giorgio Cipolloni , László Erdős , Hong Chang Ji

We prove multi-dimensional central limit theorems for the spectral moments (of arbitrary degrees) associated with random matrices with real-valued i.i.d. entries, satisfying some appropriate moment conditions. Our techniques rely on a…

Probability · Mathematics 2009-09-30 Ivan Nourdin , Giovanni Peccati

The supersymmetric technique is applied to computing the average spectral density near zero energy in the large-N limit of the random-matrix ensembles with zero eigenvalues: B, DIII-odd, and the chiral ensembles (classes AIII, BDI, and…

Condensed Matter · Physics 2009-11-07 D. A. Ivanov

We compute the large scale (macroscopic) correlations in ensembles of normal random matrices with an arbitrary measure and in ensembles of general non-Hermition matrices with a class of non-Gaussian measures. In both cases the eigenvalues…

High Energy Physics - Theory · Physics 2008-11-26 P. Wiegmann , A. Zabrodin

A variant for the Hilbert and Polya spectral interpretation of the Riemann zeta function is proposed. Instead of looking for a self-adjoint linear operator H, whose spectrum coincides with the Riemann zeta zeros, we look for the complex…

High Energy Physics - Theory · Physics 2007-05-23 S. Joffily

Statistical properties of ensembles of random density matrices are investigated. We compute traces and von Neumann entropies averaged over ensembles of random density matrices distributed according to the Bures measure. The eigenvalues of…

Quantum Physics · Physics 2009-11-10 Hans-Juergen Sommers , Karol Zyczkowski

We introduce a random matrix framework for studying statistical-mechanical lattice systems through spectral observables. Equilibrium configurations sampled from a Boltzmann measure are mapped to matrix ensembles whose covariance structure…

Disordered Systems and Neural Networks · Physics 2026-05-21 Yaprak Önder , Abbas Ali Saberi , Roderich Moessner

We calculate eigenvector statistics in an ensemble of non-Hermitian matrices describing open quantum systems [F. Haake et al., Z. Phys. B 88, 359 (1992)] in the limit of large matrix size. We show that ensemble-averaged eigenvector…

Mesoscale and Nanoscale Physics · Physics 2009-10-31 B. Mehlig , M. Santer

The Riemann hypothesis states that all nontrivial zeros of the zeta function lie in the critical line $\Re(s)=1/2$. Hilbert and P\'olya suggested that one possible way to prove the Riemann hypothesis is to interpret the nontrivial zeros in…

Mathematical Physics · Physics 2014-01-29 G. Menezes , B. F. Svaiter , N. F. Svaiter

We establish universality of local eigenvalue correlations in unitary random matrix ensembles (1/Z_n) |\det M|^{2\alpha} e^{-n\tr V(M)} dM near the origin of the spectrum. If V is even, and if the recurrence coefficients of the orthogonal…

Mathematical Physics · Physics 2009-11-10 A. B. J. Kuijlaars , M. Vanlessen

We establish a general framework to explore parametric statistics of individual energy levels in unitary random matrix ensembles. For a generic confinement potential $W(H)$, we (i) find the joint distribution functions of the eigenvalues of…

Condensed Matter · Physics 2009-11-10 I. E. Smolyarenko , B. D. Simons

Let $\mathbf{Q}=(Q_1,\ldots,Q_n)$ be a random vector drawn from the uniform distribution on the set of all $n!$ permutations of $\{1,2,\ldots,n\}$. Let $\mathbf{Z}=(Z_1,\ldots,Z_n)$, where $Z_j$ is the mean zero variance one random variable…

Statistics Theory · Mathematics 2015-11-18 Zhigang Bao , Liang-Ching Lin , Guangming Pan , Wang Zhou