Quantifying the non-ergodicity of scaled Brownian motion

Abstract

We examine the non-ergodic properties of scaled Brownian motion, a non-stationary stochastic process with a time dependent diffusivity of the form $D(t)\simeq t^{\alpha -1}$. We compute the ergodicity breaking parameter EB in the entire range of scaling exponents $\alpha$, both analytically and via extensive computer simulations of the stochastic Langevin equation. We demonstrate that in the limit of long trajectory lengths $T$ and short lag times $\Delta$ the EB parameter as function of the scaling exponent $\alpha$ has no divergence at $\alpha =1/2$ and present the asymptotes for EB in different limits. We generalise the analytical and simulations results for the time averaged and ergodic properties of scaled Brownian motion in the presence of ageing, that is, when the observation of the system starts only a finite time span after its initiation. The approach developed here for the calculation of the higher time averaged moments of the particle displacement can be applied to derive the ergodic properties of other stochastic processes such as fractional Brownian motion.

19 pages, 6 figures (IOP LaTeX)

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• Single-trajectory spectral analysis of scaled Brownian motion
• Bayesian inference of scaled versus fractional Brownian motion
• Leveraging large-deviation statistics to decipher the stochastic properties of measured trajectories
• Ergodic property of Langevin systems with superstatistical, uncorrelated or correlated diffusivity
• Ergodic property of random diffusivity system with trapping events
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