论文标题

随机矩阵产品的局部光谱统计的通用性

Universality of local spectral statistics of products of random matrices

论文作者

Akemann, Gernot, Burda, Zdzislaw, Kieburg, Mario

论文摘要

我们得出了$ m $ m $ ginibre矩阵的奇异值的相关函数的精确分析表达式,在双缩放限制限制$ m,n \ rightarrow \ rightarrow \ infty $中的大小$ n $。奇异值统计数据是通过确定点过程的核心来描述的,该过程的内核在GUE统计和Dirac-Delta(纠察线)统计范围之间进行了插值。在热力学限制中,$ n \ rightarrow \ infty $,插值参数由极限商$ a = n/m $给出。我们的目标之一是在硬边缘,散装和柔软的边缘上找到一种明确的内核形式,以供任何$ a $。我们发现,除了标准缩放机制外,还有一个新的过渡方案,该机制在硬边和批量之间进行了插值。我们猜想这些结果是普遍的,并且它们适用于从吸引力的高斯盆地(包括相关矩阵)的一系列随机矩阵产物。我们通过数值模拟证实了这一猜想。此外,我们表明,所考虑的随机矩阵产物的局部光谱统计量与戴森·布朗尼运动的局部统计量相同,其初始条件由等距{位置,}给出,至关重要的差异,即这种等价仅在本地存在。最后,我们已经在软边缘确定了介质光谱尺度,这对于光谱的展开至关重要。

We derive exact analytical expressions for correlation functions of singular values of the product of $M$ Ginibre matrices of size $N$ in the double scaling limit $M,N\rightarrow \infty$. The singular value statistics is described by a determinantal point process with a kernel that interpolates between GUE statistic and Dirac-delta (picket-fence) statistic. In the thermodynamic limit, $N\rightarrow \infty$, the interpolation parameter is given by the limiting quotient $a=N/M$. One of our goals is to find an explicit form of the kernel at the hard edge, in the bulk and at the soft edge for any $a$. We find that in addition to the standard scaling regimes, there is a new transitional regime which interpolates between the hard edge and the bulk. We conjecture that these results are universal, and that they apply to a broad class of products of random matrices from the Gaussian basin of attraction, including correlated matrices. We corroborate this conjecture by numerical simulations. Additionally, we show that the local spectral statistics of the considered random matrix products is identical with the local statistics of Dyson Brownian motion with the initial condition given by equidistant {positions,} with the crucial difference that this equivalence holds only locally. Finally, we have identified a mesoscopic spectral scale at the soft edge which is crucial for the unfolding of the spectrum.

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