Roughness distributions for 1Õf signals

The probability density function ~PDF! of the roughness, i.e., of the temporal variance, of 1/f a noise signals is studied. Our starting point is the generalization of the model of Gaussian, time periodic, 1/f noise, discussed in our recent Letter @Phys. Rev. Lett. 87, 240601 ~2001!#, to arbitrary power law. We investigate three main scaling regions (a<1/2, 1/2,a<1, and 1,a), distinguished by the scaling of the cumulants in terms of the microscopic scale and the total length of the period. Various analytical representations of the PDF allow for a precise numerical evaluation of the scaling function of the PDF for any a . A simulation of the periodic process makes it possible to study also nonperiodic, thus experimentally more relevant, signals on relatively short intervals embedded in the full period. We find that for a<1/2 the scaled PDFs in both the periodic and the nonperiodic cases are Gaussian, but for a.1/2 they differ from the Gaussian and from each other. Both deviations increase with growing a . That conclusion, based on numerics, is reinforced by analytic results for a52 and a→` , in the latter limit the scaling function of the PDF being finite for periodic signals, but developing a singularity for the aperiodic ones. Finally, an overview is given for the scaling of cumulants of the roughness and the various scaling regions in arbitrary dimensions. We suggest that our theoretical and numerical results open a different perspective on the data analysis of 1/f a processes.