Height distributions in interface growth: The role of the averaging process
Tiago J. Oliveira

TL;DR
This paper investigates how different averaging methods affect the estimation of height distribution statistics in interface growth models, revealing that 1-point statistics better capture universality in steady states despite finite-size effects.
Contribution
It demonstrates that 1-point statistical analysis reduces finite-size and finite-time effects in height distribution measurements, but fails to reveal universality in fluctuating steady states.
Findings
1-point statistics significantly reduce finite-size effects.
Interface statistics exhibit strong finite-time effects.
Analysis of height fluctuations reveals universality in steady states.
Abstract
To quantitatively characterize height distributions (HDs), one uses adimensional ratios of their first central moments () or cumulants (), especially the skewness and kurtosis , whose accurate estimate demands an averaging over all points of the height profile at a given time, in translation-invariant interfaces, and over independent samples. One way of doing this is by calculating [or ] for each sample and then carrying out an average of them for the interfaces, with and being calculated only at the end. Another approach consists in directly calculating the ratios for each interface and, then, averaging the values. It turns out, however, that and for the growth regime HDs display strong finite-size and -time effects when estimated from these "interface statistics", as already observed in some previous works…
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