Counting function fluctuations and extreme value threshold in multifractal patterns: the case study of an ideal $1/f$ noise
Yan V. Fyodorov, Pierre Le Doussal, Alberto Rosso

TL;DR
This paper analyzes fluctuations and extreme value thresholds in multifractal patterns generated by ideal $1/f$ noise, revealing universal scaling laws and providing exact formulas for the statistics of high values.
Contribution
It introduces a detailed analytical and numerical study of extreme values in ideal $1/f$ noise, uncovering universal scaling behaviors and deriving formulas for the maximum intensity and counting function.
Findings
The typical threshold for extreme values scales as $2 - c\ln\ln M/\ln M$ with $c=3/2$.
The distribution of high values exhibits a power-law tail affecting mean and typical counts.
Exact formulas for the mean and variance of the counting function are derived for $1/f$ noise.
Abstract
To understand the sample-to-sample fluctuations in disorder-generated multifractal patterns we investigate analytically as well as numerically the statistics of high values of the simplest model - the ideal periodic Gaussian noise. By employing the thermodynamic formalism we predict the characteristic scale and the precise scaling form of the distribution of number of points above a given level. We demonstrate that the powerlaw forward tail of the probability density, with exponent controlled by the level, results in an important difference between the mean and the typical values of the counting function. This can be further used to determine the typical threshold of extreme values in the pattern which turns out to be given by with . Such observation provides a rather compelling explanation of the mechanism behind universality of…
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Taxonomy
TopicsTheoretical and Computational Physics · Complex Systems and Time Series Analysis · Statistical Mechanics and Entropy
