Elsevier Science Home
Computer Physics Communications Program Library
Full text online from Science Direct
Programs in Physics & Physical Chemistry
CPC Home

[Licence| Download | New Version Template] aedz_v1_0.tar.gz(2323 Kbytes)
Manuscript Title: Library of sophisticated functions for analysis of nuclear spectra
Authors: Miroslav Morhác, Vladislav Matousek
Program title: SpecAnalysLib 1.1
Catalogue identifier: AEDZ_v1_0
Distribution format: tar.gz
Journal reference: Comput. Phys. Commun. 180(2009)1913
Programming language: C++.
Computer: Pentium 3 PC 2.4 GHz or higher, Borland C++ Builder v. 6. A precompiled Windows version is included in the distribution package.
Operating system: Windows 32 bit versions.
RAM: 10 MB
Word size: 32 bits
Keywords: Nuclear spectra analysis, Background elimination, Smoothing, Peak searching, Deconvolution, Unfolding, Fitting.
PACS: 07.05.Hd, 29.85.+c, 07.05.Rm.
Classification: 17.6.

Nature of problem:
The demand for advanced highly effective experimental data analysis functions is enormous. The library package represents one approach to give the physicists the possibility to use the advanced routines simply by calling them from their own programs. SpecAnalysLib is a collection of functions for analysis of one- and two-parameter γ-ray spectra, but they can be used for other types of data as well. The library consists of sophisticated functions for background elimination, smoothing, peak searching, deconvolution, and peak fitting.

Solution method:
The algorithms of background estimation are based on Sensitive Nonlinear Iterative Peak (SNIP) clipping algorithm. The smoothing algorithms are based on the convolution of the original data with several types of filters and algorithms based on discrete Markov chains. The peak searching algorithms use the smoothed second differences and they can search for peaks of general form. The deconvolution (decomposition - unfolding) functions use the Gold iterative algorithm, its improved high resolution version and Richardson-Lucy algorithm. In the algorithms of peak fitting we have implemented two approaches. The first one is based on the algorithm without matrix inversion - AWMI algorithm. It allows it to fit large blocks of data and large number of parameters. The other one is based on the calculation of the system of linear equations using Stiefel-Hestens method. It converges faster than the AWMI, however it is not suitable for fitting large number of parameters.

Dimensionality of the analysed data is limited to two.

Unusual features:
Dynamically loadable library (DLL) of processing functions users can call from their own programs.

Running time:
Most processing routines execute interactively or in a few seconds. Computationally intensive routines (deconvolution, fitting) execute longer, depending on the number of iterations specified and volume of the processed data.