Library of sophisticated functions for analysis of nuclear spectra
In the paper we present compact library for analysis Of nuclear spectra. The library consists of sophisticated functions for background elimination, smoothing, peak searching, deconvolution, and peak fitting. The functions can process one- and two-dimensional spectra. The software described in the paper comprises a number of conventional as well as newly developed methods needed to analyze experimental data. Program summary Program title: SpecAnalysLib 1.1 Catalogue identifier: AEDZ_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEDZ_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 42154 No. of bytes in distributed program, including test data, etc.: 2379437 Distribution format: tar.gz 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 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 gamma-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 Non-linear 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. Restrictions: Dimensionality of the analyzed 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. (C) 2009 Elsevier B.V. All rights reserved.
background elimination smoothing peak searching deconvolution fitting unfolding nuclear spectra analysis