Random Matrix Spectra as a Time Series
Chaotic Dynamics
2013-12-12 v2
Abstract
Spectra of ordered eigenvalues of finite Random Matrices are interpreted as a time series. Dataadaptive techniques from signal analysis are applied to decompose the spectrum in clearly differentiated trend and fluctuation modes, avoiding possible artifacts introduced by standard unfolding techniques. The fluctuation modes are scale invariant and follow different power laws for Poisson and Gaussian ensembles, which already during the unfolding allows to distinguish the two cases.
Cite
@article{arxiv.1311.5553,
title = {Random Matrix Spectra as a Time Series},
author = {Ruben Fossion and Gamaliel Torres Vargas and Juan Carlos López Vieyra},
journal= {arXiv preprint arXiv:1311.5553},
year = {2013}
}
Comments
Phys. Rev. E (Rapid Communication) [http://pre.aps.org/accepted/ca072R0eWe4Ebf1410bf6d37595695d4a4c8cd76f] Accepted for publication, Wednesday Nov 20, 2013