A NEW SMOOTHING ALGORITHM FOR STATISTICAL NOISE-REDUCTION

A new, general, high quality smoothing algorithm is presented. It is based on a polynomial fitting of the real and imaginary components of the Fourier transformed spectra. Such fits, after inverse transformation into the real space, are shown to drastically reduce the statistical noise present in some experimental spectra and offer a fast and simple method for smoothing. The smoothing principles of this algorithm were applied, for demonstration of the quality of the fits which can be obtained, to Rutherford backscattering (RBS) and to particle induced X-ray emission (PIXE) spectra.