Abstract

Random walks in random sceneries (RWRS) are simple examples of stochastic processes in disordered media. They were introduced at the end of the 70's by Kesten-Spitzer and Borodin, motivated by the construction of new self-similar processes with stationary increments. Two sources of randomness enter in their definition: a random field $\xi = (\xi(x))_{x \in \mathbb{Z}^d}$ of i.i.d. random variables, which is called the random scenery, and a random walk $S = (S_n)_{n \in \mathbb{N}}$ evolving in $\mathbb{Z}^d$, independent of the scenery. The RWRS $Z = (Z_n)_{n \in \mathbb{N}}$ is then defined as the accumulated scenery along the trajectory of the random walk, i.e., $Z_n := \sum_{k=1}^n \xi(S_k)$. The law of $Z$ under the joint law of $\xi$ and $S$ is called annealed'', and the conditional law given $\xi$ is called quenched''. Recently, functional central limit theorems under the quenched law were proved for $Z$ by the first two authors for a class of transient random walks including walks with finite variance in dimension $d \ge 3$. In this paper we extend their results to dimension $d=2$.

Highlights

  • Let d ≥ 1 and (ξ(x))x∈Zd be a collection of independent and identically distributed (i.i.d.) real random variables, further referred to as scenery, and (Sn)n≥0 a random walk evolving in Zd, independent of the scenery

  • The random walk in random scenery (RWRS) is the process obtained by adding up the values of the scenery seen by the random walk along its trajectory, that is, Zn =

  • RWRS appears naturally in a variety of contexts, for instance (i) in the energy function of statistical mechanics models of polymers interacting with a random medium, (ii) in Bouchaud’s trap model via the clock process, see [2], (iii) in the study of random walks in randomly oriented lattices, as in [8, 11]

Read more

Summary

Introduction

Let d ≥ 1 and (ξ(x))x∈Zd be a collection of independent and identically distributed (i.i.d.) real random variables, further referred to as scenery, and (Sn)n≥0 a random walk evolving in Zd, independent of the scenery. The case of a transient random walk (i.e., a < d) has been treated in [10] (see [29, 23, 7]): rescaling by n1/b one obtains as limit a stable process of index b. The first two authors√proved in [21] that a quenched functional central limit theorem (with the usual n-scaling and Gaussian law in the limit) holds for a class of transient random walks. The aim of this paper is to prove the following quenched functional central limit theorem. Sn n n converges in distribution to a random variable with characteristic function given by t → exp(−a|t|), a > 0

Two-dimensional random walks
Truncation
Control of the truncated version
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call