The CLEANest and SLICK methods calculate the power spectrum of unequally-spaced data using an advanced implementation of the Date Compensated Discrete Fourier Transform (DCDFT). CLEANest is a particularly effective technique for detecting and describing multi periodic signals.

Peranso implements the CLEANest algorithm as described by Grant Foster1. In addition, Peranso implements the SLICK method, which is a very useful tool for extracting multiple signal components from a given data set. SLICK iteratively searches for multiple frequencies in a given signal, and attempts to find a "best-fitting ensemble" of frequencies. SLICK will adjust each "found" frequency such that overall signal strength is maximized. Both methods are combined in one convenient Peranso dialog box, called the CLEANest Workbench

A CLEANest calculation is started from the CLEANest Period Determination dialog box, which is similar to the Lomb-Scargle dialog box. Subsequent refinements  can be made iteratively using the CLEANest Workbench. Prominent periods of the Period Window appear as peaks.


The CLEANest spectrum is not truly a spectrum, but a composite graphical representation of two sets of information : (a) the optimal discrete Fourier representation of the data (the so called discrete spectrum), and (b) the Fourier transform of the residuals (the so called residual spectrum). 
The discrete spectrum is formed by the individual amplitudes of each frequency component that is used to construct the CLEANest model function. They are represented by vertical lines in the spectrum, drawn at the identified frequencies, and have no width (only an amplitude). 
The residual spectrum is obtained by subtracting the model function from the original data and Fourier analyzing the residuals by a DCDFT.

(1) Foster, G., 1995, Astron. J., 109, 1889