A genetic algorithm approach for multiplet deconvolution in γ-ray spectra

García-Talavera, M.; Ulicny, B.

doi:10.1016/s0168-9002(03)02052-7

Cited by 25 publications

(14 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…18 The GA was first used to find the search space of a set of parameters, and then a grid search applied to climb up the local hills. Artificially generated fourpeak multiplets with varying peak heights ratios were used and an adequate photopeak library has to be built before the program is used.…”

Section: Application Of Genetic Algorithms To Xrsmentioning

confidence: 99%

Chemometrics and its applications to x‐ray spectrometry

Luo

2006

X-Ray Spectrometry

View full text Add to dashboard Cite

Recent developments in curve fitting, multivariate calibration, and pattern recognition in chemometrics, and their application to x-ray spectrometry, are reviewed. Relatively innovated algorithms, namely genetic algorithms, neural networks and support vector machines, are discussed. Together with the three algorithms, the performances of different algorithms are compared briefly, which mainly includes principal component analysis, partial least-squares regression, factor analysis, cluster analysis, nearest neighbor methods, linear discriminant analysis, linear learning machine, and soft independent modeling of class analogy. In general, the chemometrics methods are superior to the conventional methods, such as Fourier transform and Marquardt-Levenberg algorithms, to a certain extent.

show abstract

Section: Application Of Genetic Algorithms To Xrsmentioning

confidence: 99%

Chemometrics and its applications to x‐ray spectrometry

Luo

2006

X-Ray Spectrometry

View full text Add to dashboard Cite

show abstract

“…Compared to previous algorithms used for spectral decomposition~McIntosh et al, 1998;Ramìrez & Fuentes, 2002;Garcia-Talavera & Ulicny, 2003!, our novel fitting code Genetic Algorithm for SPEctral Decomposition~GASPED! implements several new features extending the range of the problems solved, improves the convergence rate and provides a graphical user interface for more effective interactive analysis of the resulting fits.…”

Section: Gasped Codementioning

confidence: 99%

Genetic algorithms in spectroscopic diagnostics of hot dense plasmas

et al. 2006

View full text Add to dashboard Cite

This paper will present a novel genetic-algorithm-based code~GASPED!, developed for the analysis of fine features e.g., satellite structure and line shifts! in X-ray spectra emitted by hot dense plasmas. The problem dependent modification of standard genetic-algorithm concepts allows efficient decomposition of spectra in concrete physical terms, such as resonance and intercombination lines, dielectronic satellites, or prospective nuclear transitions. Two examples of the code application demonstrate the proposed approach. High resolution K-shell spectra emitted from Heand Li-like Al ions immersed in dense, constrained-flow plasma are decomposed into individual pseudo-Voigt components, by using anticipatory theoretical knowledge of the satellite structure simulated by the multilevel collisionalradiative code~MARIA!. Line shifts of the He-like resonance and intercombination line are deduced assuming the aggregate plasma-induced shifts of the parent lines and their satellites. The trend in the frequency shifts observed as a function of the variable plasma parameters qualitatively follows the theoretical predictions. The found variations of the exchange energy between the singlet and triplet levels provide a new impact for the line shift theories. The second example concerns the search for low-lying nuclear transitions in hot dense laser-produced plasmas. The spectra of highly ionized Ta are decomposed by combining the GASPED code with results of ab initio atomic data calculations performed by the RELAC code. Upper limits for observation of the controversial radiative decay of Ta nuclei at 6.238 eV are estimated.

show abstract

“…Therefore, high frequencies, which are related to noise, can be localized and controlled at different levels of wavelet decomposition. The multiscale description of signals has facilitated the development of wavelet theory and its application to numerous fields (Averbuch & Zheludev, 2009;Charles et al, 2004;Fan & Koo, 2002;Neelamani et al, 2004;Zheludev, 1999;Rashed et al, 2007;Garcia-Talavera et al, 2003;Starck et al, 2003;Jammal et al, 2004;Rucka et al, 2006). This chapter is intended to explore capabilities of wavelets for the deconvolution framework.…”

Section: Introductionmentioning

confidence: 99%