Anomalous diffusion in the dynamics of complex processes

TL;DR

This paper introduces an interpolation method using the chaotic difference moment to identify anomalous diffusion in complex signals at large time scales, demonstrated on various natural signals.

Contribution

It proposes a novel procedure for detecting anomalous diffusion in steady-state complex signals by fitting the chaotic difference moment to an interpolation expression.

Findings

Interpolation accurately describes chaotic dynamics in signals

Anomalous diffusion is present in magnetoencephalograms, quantum dots, and X-ray emissions

Method broadens detection of anomalous diffusion in natural processes

Abstract

Anomalous diffusion, process in which the mean-squared displacement of system states is a non-linear function of time, is usually identified in real stochastic processes by comparing experimental and theoretical displacements at relatively small time intervals. This paper proposes an interpolation expression for the identification of anomalous diffusion in complex signals for the cases when the dynamics of the system under study reaches a steady state (large time intervals). This interpolation expression uses the chaotic difference moment (transient structural function) of the second order as an average characteristic of displacements. A general procedure for identifying anomalous diffusion and calculating its parameters in real stochastic signals, which includes the removal of the regular (low-frequency) components from the source signal and the fitting of the chaotic part of the experimental difference moment of the second order to the interpolation expression, is presented. The procedure was applied to the analysis of the dynamics of magnetoencephalograms, blinking fluorescence of quantum dots, and X-ray emission from accreting objects. For all three applications, the interpolation was able to adequately describe the chaotic part of the experimental difference moment, which implies that anomalous diffusion manifests itself in these natural signals. The results of this study make it possible to broaden the range of complex natural processes in which anomalous diffusion can be identified. The relation between the interpolation expression and a diffusion model, which is derived in the paper, allows one to simulate the chaotic processes in the open complex systems with anomalous diffusion.