V. Matoušek

Background elimination methods for multidimensional coincidence γ-ray spectra

Abstract In the paper new methods to separate useful information from background in one, two, three and multidimensional spectra (histograms) measured in large multidetector γ-ray arrays are derived. The sensitive nonlinear peak clipping algorithm is the basis of the methods for estimation of the background in multidimensional spectra. The derived procedures are simple and therefore have a very low cost in terms of computing time.

Efficient one- and two-dimensional gold deconvolution and its application to γ-ray spectra decomposition

Abstract The use of deconvolution operation makes it possible to improve the resolution in γ-ray spectra. The paper describes a non-oscillating method of Gold deconvolution. The method is generalized for multidimensional spectra. A new optimized algorithm of one- and two-dimensional Gold deconvolution aimed to reduce number of computer operations has been derived in the paper.

Identification of peaks in multidimensional coincidence γ-ray spectra

Abstract In the paper a new algorithm to find peaks in two, three and multidimensional spectra, measured in large multidetector γ-ray arrays, is derived. Given the dimension m, the algorithm is selective to m-fold coincidence peaks. It is insensitive to intersections of lower-fold coincidences, hereinafter called ridges.

Peak Clipping Algorithms for Background Estimation in Spectroscopic Data

In this paper we present sensitive nonlinear iterative peak (SNIP) clipping algorithms to estimate background in various kinds of spectra. We have proposed several improvements of the SNIP algorithm. Moreover, we have extended and generalized the SNIP algorithm for multidimensional coincidence γ-ray spectra. In addition to the continuous background, the so-called lower-order coincidences, i.e., coincidences peak-background in different dimensions, must also be included into the estimated background. Further, we have derived a set of modifications of the algorithm that allow estimation of specific shapes of background and ridges as well.

Complete positive deconvolution of spectrometric data

In the paper new algorithm for complete non-oscillating deconvolution of the spectroscopic data is derived. It is based on Tikhonov regularization of negative elements of the data being deconvolved. It is able to decompose completely multiplets of peaks practically to @d-functions, where standard algorithms fail. The proposed procedure is rather simple and therefore it can be easily implemented in any software aimed for the analysis of spectroscopic data.

High-resolution boosted deconvolution of spectroscopic data

The quality of the analysis of spectrometric data consists in correct identification of the existence of peaks and subsequently in good estimation of their positions. In this paper we present high-resolution deconvolution algorithms. We propose an improvement in the efficiency by introducing a boosting operation into the deconvolution process.

Integrated multiparameter nuclear data analysis package

Abstract A modular system to realize measurements along with analysis, processing and visualization of acquired data is described. Besides standard methods also new developed algorithms of data processing have been included in the system.

The 270 MeV deuteron beam polarimeter at the Nuclotron Internal Target Station

A deuteron beam polarimeter has been constructed at the Internal Target Station at the Nuclotron of JINR. The polarimeter is based on spin-asymmetry measurements in the d − p elastic scattering at large angles and the deuteron kinetic energy of 270 MeV. It allows to measure vector and tensor components of the deuteron beam polarization simultaneously.

A new method of on-line multiparameter amplitude analysis with compression

Abstract An algorithm of on-line multidimensional amplitude analysis with compression using fast adaptive orthogonal transform is presented in the paper. The method is based on a direct modification of multiplication coefficients of the signal flow graph of the fast Cooley-Tukeys algorithm. The coefficients are modified according to a reference vector representing the processed data. The method has been tested to compress three parameter experimental nuclear data. The efficiency of the derived adaptive transform is compared with classical orthogonal transforms.

An adaptive fast transform algorithm for multi-dimensional data compression

Abstract In the paper an original method of construction special orthogonal transform for data compression has been proposed. This method is based on a direct modification of multiplication coefficients of the signal flow graph of the fast Cooley-Tukeys algorithm. The transform matrix is constructed according to the reference vector calculated from the processed data. Using this transform the compression method for multi-dimensional data has been proposed. The method has been proved effective for nuclear physics data. The results obtained using the derived transform and the classical transforms have been compared.