Localization properties of the anomalous diffusion phase $x ~ t^{\mu}$ in the directed trap model and in the Sinai diffusion with bias

TL;DR

This paper develops a generalized real space renormalization approach to analyze the anomalous diffusion phase in Sinai diffusion with bias and directed trap models, providing exact series expansions and detailed localization properties.

Contribution

It introduces a generalized RSRG method for finite ermib0, allowing for the spreading of the thermal packet over multiple valleys and deriving exact series expansions of observables.

Findings

Explicit calculation of the diffusion front and localization parameter up to order ermib0^2.

Derivation of localization parameters Y_k for arbitrary k.

Mapping between Sinai diffusion with bias and directed trap models.

Abstract

We study the anomalous diffusion phase $x ~ t^{\mu}$ with $0<\mu<1$ which exists both in the Sinai diffusion at small bias, and in the related directed trap model presenting a large distribution of trapping time $p(\tau) \sim 1/\tau^{1+\mu}$. Our starting point is the Real Space Renormalization method in which the whole thermal packet is considered to be in the same renormalized valley at large time : this assumption is exact only in the limit $\mu \to 0$ and corresponds to the Golosov localization. For finite $\mu$, we thus generalize the usual RSRG method to allow for the spreading of the thermal packet over many renormalized valleys. Our construction allows to compute exact series expansions in $\mu$ of all observables : at order $\mu^n$, it is sufficient to consider a spreading of the thermal packet onto at most $(1+n)$ traps in each sample, and to average with the appropriate measure over the samples. For the directed trap model, we show explicitly up to order $\mu^2$ how to recover the diffusion front, the thermal width, and the localization parameter $Y_2$. We moreover compute the localization parameters $Y_k$ for arbitrary $k$, the correlation function of two particles, and the generating function of thermal cumulants. We then explain how these results apply to the Sinai diffusion with bias, by deriving the quantitative mapping between the large-scale renormalized descriptions of the two models.