Speaker
Vladislav Matoušek
(Institute of Physics, Slovak Academy of Sciences)
Description
The deconvolution methods are very efficient and widely used tools to improve the resolution in the spectrometric data. They are of great importance mainly in the tasks connected with decomposition of low amplitude overlapped peaks (multiplets) in the presence of noise.
In the talk we will present a set of deconvolution algorithms and a study of their decomposition capabilities from the resolution point of view. We have proposed improvements in the efficiency of the iterative deconvolution methods by introducing further modifications into deconvolution process, e.g. noise suppression operations during iterations and improved blind deconvolution methods. We will illustrate their suitability for processing of noisy spectrometric data. It will be shown, that using the new developed algorithms we are able to improve the resolution in spectrometric data. The methods are able better detect hidden peaks in the noisy gamma-ray spectra and decompose the overlapped peaks by concentrating the peak areas into a few channels. The efficiency of the above mentioned algorithms and their comparison will be presented also.
Primary author
Vladislav Matoušek
(Institute of Physics, Slovak Academy of Sciences)
Co-author
Miroslav Morhac
(Institute of Physics, Slovak Academy of Sciences)
