
The McCarthy multimatrix ensemble of random matrices. For all integers n≥1 and d≥1, let Mn,d be the set of d-tuples (M1,…,Md) of n×n Hermitian matrices such that
MpMq=MqMpfor all 1≤p,q≤d. We equip this hypersurface with the trace of the Gaussian distribution, namely
(M1,…,Md)↦1Zn,de−∑dk=1Tr(M2k)dM where dM is the trace on Mn,d of the (product) Lebesgue measure on d-uples of n×n Hermitian matrices, and Zn,d is the normalizing constant. Since the Hermitian matrices M1,…,Md commute, they are diagonalizable in the same orthonormal basis, namely there exists a single n×n unitary matrix U and real n×n diagonal matrices D1,…,Dd carrying the eigenvalues of M1,…,Md respectively such that
U(M1,…,Md)U∗=(D1,…,Dd). The eigenvectors couple the eigenvalues : λi∈Rd is an eigenvalue of (M1,…,Md) when there exists u∈Rn, u≠0, such that Mpu=λi,pu for all 1≤p≤n. The computation of the Jacobbian of the spectral change of variable provides the following remarkable fact: the joint law of the eigenvalues λ1,…,λn of (M1,…,Md) has probability density function
(λ1,…,λn)∈(Rd)n↦1Zn,de−∑ni=1‖ where \mathrm{d}\lambda is the Lebesgue measure on (\mathbb{R}^d)^n, \|x\|^2:=x_1^2+\cdots+x_d^2 is the squared Euclidean norm of \mathbb{R}^d, and Z_{n,d} is the normalizing constant. When d=1, we recover the usual formula for the Gaussian Unitary Ensemble (GUE), studied notably by Freeman Dyson. Thus the model generalizes the GUE into a MacCarthy Unitary Ensemble'' (MUE). When d=2, we obtain the formula for the spectrum of the complex Ginibre ensemble, providing a novel interpretation of this formula in terms of spectrum of two commuting Hermitian matrices!
The log-gas picture and the analogue of the Wigner theorem. The law above is a log-gas of n particles on \mathbb{R}^d with quadratic external field, namely
\mathrm{e}^{-\sum_{i=1}^n\|\lambda_i\|^2} \prod_{1\leq i< j\leq n}\|\lambda_i-\lambda_j\|^2 =\exp\Bigr(n\int V\mathrm{d}\mu_n+\iint_{\neq}W\mathrm{d}\mu_n^{\otimes 2}\Bigr) where
V(x):=\|x\|^2,\quad W(x,y):=\log\frac{1}{\|x-y\|},\quad \mu_n:=\frac{1}{n}\sum_{i=1}^n\delta_{\lambda_i}. What is the behavior of the empirical measure \mu_n under this law? By computing the correlation functions, or by using the Laplace method, we get that after scaling by n^{-1/2}, the empirical measure in high dimension n tends to the equilibrium measure
\mu_{\mathrm{eq}} =\arg\min_\mu\Bigr(\int V\mathrm{d}\mu+\iint W\mathrm{d}\mu^{\otimes 2}\Bigr) where the minimum runs over the set of probability measures on \mathbb{R}^d. It turns out that these equibrium measures were already computed, quite recently for d\geq3, namely
\mu_{\mathrm{eq}} = \begin{cases} \frac{1}{\pi} \sqrt{2-x^2} \mathbf{1}_{|x|\leq\sqrt{2}} & \text{if $d=1$}\\ \frac{1}{\pi} \mathbf{1}_{\|x\|\leq1} & \text{if $d=2$}\\ \frac{3}{2\pi^2}\frac{1}{\sqrt{\frac{2}{3}-\|x\|^2}}\mathbf{1}_{\|x\|\leq\sqrt{\frac{2}{3}}} &\text{if $d=3$}\\ \sigma_{S^{d-1}(\frac{1}{\sqrt{2}})} & \text{if $d\geq 4$} \end{cases} where \sigma_{S^{d-1}(R)} is the uniform distribution on the sphere S^{d-1}:=\{x\in\mathbb{R}^d:\|x\|=R\} of radius R. Also \mu_{\mathrm{eq}} is radially symmetric and its one-dimensional projections are semi-circle distributions. We have an analogue or generalization of the Wigner theorem for the McCarthy multimatrix Ensemble. The Wigner theorem for GUE corresponds to d=1.
Note. Lydia Giacomin is studying these questions as a side project during her PhD.
Further reading.
- John E. McCarthy
Random commuting matrices
Preprint (2023) - John E. McCarthy and Hazel T. McCarthy
Random anti-commuting Hermitian matrices
Preprint (2023) - Peter Elbau and Giovanni Felder
Density of eigenvalues of random normal matrices
Communications in Mathematical Physis (2005) - Djalil Chafai, Edward B. Saff, and Robert S. Womersley
On the solution of a Riesz equilibrium problem and integral identities for special functions
Journal of Mathematical Analysis and Applications (2022) - Djalil Chafai, Edward B. Saff, and Robert S. Womersley
Threshold condensation to singular support for a Riesz equilibrium problem
Analysis and Mathematical Physics (2023) - Peter J. Forrester
Log-Gases and Random Matrices
London Mathematical Society Monographs, Princeton University Press (2010)
