Product matrix processes are multi-level point processes formed by the singular values of random matrix products. In this paper, we study such processes where the products of up to m complex random matrices are no longer independent, by introducing a coupling term and potentials for each product. We show that such a process still forms a multi-level determinantal point processes, and give formulae for the relevant correlation functions in terms of the corresponding kernels. For a special choice of potential, leading to a Gaussian coupling between the mth matrix and the product of all previous m- 1 matrices, we derive a contour integral representation for the correlation kernels suitable for an asymptotic analysis of large matrix size n. Here, the correlations between the first m- 1 levels equal that of the product of m- 1 independent matrices, whereas all correlations with the mth level are modified. In the hard edge scaling limit at the origin of the spectra of all products, we find three different asymptotic regimes. The first regime corresponding to weak coupling agrees with the multi-level process for the product of m independent complex Gaussian matrices for all levels, including the m-th. This process was introduced by one of the authors and can be understood as a multi-level extension of the Meijer G-kernel introduced by Kuijlaars and Zhang. In the second asymptotic regime at strong coupling the point process on level m collapses onto level m- 1 , thus leading to the process of m- 1 independent matrices. Finally, in an intermediate regime where the coupling is proportional to n12, we obtain a family of parameter-dependent kernels, interpolating between the limiting processes in the weak and strong coupling regime. These findings generalise previous results of the authors and their coworkers for m= 2.
Bibliographical noteFunding Information:
We are very grateful to Marco Bertola, Tomasz Checinski and Mario Kieburg for discussions and useful comments. The anonymous referee is also thanked for several comments and corrections. This work was supported by the DFG through Grant AK35/2-1 and CRC 1283 “Taming uncertainty and profiting from randomness and low regularity in analysis, stochastics and their applications” (G.A.).
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