Optimal detrended fluctuation analysis as a tool for the determination of the roughness exponent of the mounded surfaces

TL;DR

This paper introduces an optimal detrended fluctuation analysis (ODFA) method to accurately determine the roughness exponent of mounded surfaces in non-equilibrium growth models, improving upon standard DFA techniques.

Contribution

The paper develops and validates an optimal DFA method that effectively measures the roughness exponent in complex surface growth models, especially in the nMBE universality class.

Findings

ODFA accurately estimates the roughness exponent in nMBE models.

The method reveals transient anomalous scaling consistent with other nMBE models.

ODFA outperforms standard DFA and nondetrended analysis in surface roughness characterization.

Abstract

We present an optimal detrended fluctuation analysis (DFA) and applied it to evaluate the local roughness exponent in non-equilibrium surface growth models with mounded morphology. Our method consists in analyzing the height fluctuations computing the shortest distance of each point of the profile to a detrending curved that fits the surface within the investigated interval. We compare the optimal DFA (ODFA) with both the standard DFA and nondetrended analysis. We validate the ODFA method considering a one-dimensional model in the Kardar-Parisi-Zhang universality class starting from a mounded initial condition. We applied the methods to the Clarke-Vvdensky (CV) model in $2+1$ dimensions with thermally activated surface diffusion and absence of step barriers. It is expected that this model belongs to the nonlinear Molecular Beam Epitaxy (nMBE) universality class. However, an explicit observation of the roughness exponent in agreement with the nMBE class was still missing. The effective roughness exponent obtained with ODFA agrees with the value expected for nMBE class whereas using the other methods it does not. We also characterized the transient anomalous scaling of the CV model and obtained that the corresponding exponent is in agreement with the value reported for other nMBE models with weaker corrections to the scaling.