## analogue of GUE and Ginibre in higher dimensions

This is a completely unmotivated question, but what happens to the 1-point marginal distribution for the following $N$-point joint distribution: $$\displaystyle p(z_1,\ldots, z_N) = C_N \exp\left(-\sum_{j=1}^N \|z_j\|^2\right) \prod_{j < k} \|z_j - z_k\|^2$$ Here $z_j$'s are points in $\mathbb{R}^3$ or higher. Presumably one can no longer write the Vandermonde as a determinant so orthogonal polynomial theory breaks down. But I am interested in the distribution of $\|z_1\|$ as $N$ goes to infinity in this case, which shouldn't need the full machinery of orthogonal polynomials and determinantal point processes (but maybe it is still determinantal?).

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By $\|z_1\|$, I understand you mean the maximal modulus of the $z_i$'s.

If you are interested in the process of the $\|z_i\|$'s, you have no chance for a determinantal structure since you may have two different $z_i$'s with same modulus with non zero probability.

Concerning the process of the $z_i$'s, note that even if the Ginibre Ensemble (that is the case $\mathbb{R}^2$) is indeed determinantal, its kernel is related to the polynomials orthogonal with $\exp(-\|x\|^2/2)$, that is the $(z^k)_{k\geq 0}$ ... which have trivial zeros ! My point is that except on $\mathbb{R}$ you won't get so much information concerning $\|z_1\|$ from a determinantal structure.

I don't know how prove the convergence of $\|z_1\|$, but note that from your density expression, once renormalized $z_i\rightarrow z_i/\sqrt{N}$, you still can use the Coulomb-gaz approach to characterize the global distribution of the $z_i$'s (for example by proving a large deviation principle for the empirical measure) : It is given by the unique minimizer $\mu^*$ of the functional $$\iint\log\frac{1}{\| x-y\|}d\mu(x)d\mu(y) +\frac{1}{2}\int \|x\|^2d\mu(x)$$ over probability measures $\mu$ on $\mathbb{R}^3$ (or higher). I guess that $\|z_1\|$ should converge towards $\max \big(Supp(\mu^*)\cap \mathbb{R}\big)$...

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 Hi Adrien, I guess if you have a closed form for the determinantal structure, then you can compute the marginal distribution directly right? – John Jiang Jan 25 2012 at 21:36 Hi John, I don't see what you mean exactly. Could you elaborate ? – Adrien Hardy Jan 26 2012 at 11:02

Using the normalization $\mathrm{e}^{-\|z\|^2/2}$ instead of your $\mathrm{e}^{-\|z\|^2}$ for points $z$ in $\mathbb{R}^d$, the probability distribution of the one-dimensional marginal $z$ you are interested in is $$\kappa_N\mathrm{e}^{-\|z\|^2/2}q_{N}(\|z\|^2)\mathrm{d}z,$$ where $\kappa_N$ is a positive constant and $q_N$ is a unitary polynomial of degree $N-1$. Small $N$ values of $q_N$ are $$q_1(x)=1,\qquad q_2(x)=x+d,\qquad q_3(x)=x^2+d(d+2).$$

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 Hi Didier, Could you give a reference to the unitary polynomials you mentioned? I have never encountered them before. Also do you think the point process defined in my question is actually determinantal? Thanks. – John Jiang Apr 7 2011 at 19:31 Hello John. Unfortunately I cannot suggest a reference, I did it myself. Determinantal or not? I do not know. Sorry (twice). – Didier Piau Apr 7 2011 at 23:35