Scaling Analysis of Random Walks with Persistence Lengths: Application to Self-Avoiding Walks

TL;DR

This paper introduces a novel scaling analysis method for random walks using inner persistence lengths, providing new insights into self-avoiding walks and confirming known scaling exponents through Monte Carlo simulations.

Contribution

It develops an analytical approach linking end-to-end distance and persistence length, and applies it to self-avoiding walks, demonstrating the sufficiency of path segments for scaling analysis.

Findings

The persistence length converges to a constant for large N.

Scaling corrections can be expressed as higher-order corrections to the mean square end-to-end distance.

Estimated exponents match well with existing literature.

Abstract

We develop an approach for performing scaling analysis of $N$-step Random Walks (RWs). The mean square end-to-end distance, $\langle\vec{R}_{N}^{2}\rangle$, is written in terms of inner persistence lengths (IPLs), which we define by the ensemble averages of dot products between the walker's position and displacement vectors, at the $j$-th step. For RW models statistically invariant under orthogonal transformations, we analytically introduce a relation between $\langle\vec{R}_{N}^{2}\rangle$ and the persistence length, $\lambda_{N}$, which is defined as the mean end-to-end vector projection in the first step direction. For Self-Avoiding Walks (SAWs) on 2D and 3D lattices we introduce a series expansion for $\lambda_{N}$, and by Monte Carlo simulations we find that $\lambda_{\infty}$ is equal to a constant; the scaling corrections for $\lambda_{N}$ can be second and higher order corrections to scaling for $\langle\vec{R}_{N}^{2}\rangle$. Building SAWs with typically one hundred steps, we estimate the exponents $\nu_{0}$ and $\Delta_{1}$ from the IPL behavior as function of $j$. The obtained results are in excellent agreement with those in the literature. This shows that only an ensemble of paths with the same length is sufficient for determining the scaling behavior of $\langle\vec{R}_{N}^{2}\rangle$, being that the whole information needed is contained in the inner part of the paths.