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arXiv 2109.00322math.PRmath-phmath.MP
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实非对称随机矩阵乘积的涨落和相关性

Fluctuations and correlations for products of real asymmetric random matrices

Will FitzGerald, Nick Simm

摘要:

本文研究了独立实Ginibre随机矩阵乘积的实特征值统计量。这些矩阵的元素都是实同分布的。标准高斯随机变量。对于这样的产品合奏,我们证明了渐近正态性的适当规范化的线性统计的实特征值和计算的限制方差明确在全球和介观制度。我们的证明的一个关键部分建立了一致的去相关估计相关的Pfavian点过程,从而使我们能够利用弱依赖的实特征值给出简单而快速的证明中心极限定理在相当一般的条件下。我们还建立了这些点过程的普遍性。我们计算的所有相关函数的实本征值在体,原点和光谱边缘制度的渐近极限。通过适当地加强边缘处的收敛,我们还得到了最大实特征值的极限涨落。在原点附近,我们发现新的极限分布特征的最小的正实特征值。

英文摘要:

We study the real eigenvalue statistics of products of independent real Ginibre random matrices. These are matrices all of whose entries are real i.i.d. standard Gaussian random variables. For such product ensembles, we demonstrate the asymptotic normality of suitably normalised linear statistics of the real eigenvalues and compute the limiting variance explicitly in both global and mesoscopic regimes. A key part of our proof establishes uniform decorrelation estimates for the related Pfaffian point process, thereby allowing us to exploit weak dependence of the real eigenvalues to give simple and quick proofs of the central limit theorems under quite general conditions. We also establish the universality of these point processes. We compute the asymptotic limit of all correlation functions of the real eigenvalues in the bulk, origin and spectral edge regimes. By a suitable strengthening of the convergence at the edge, we also obtain the limiting fluctuations of the largest real eigenvalue. Near the origin we find new limiting distributions characterising the smallest positive real eigenvalue.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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