Artificial neural networks applied to the analysis of synchrotron nuclear resonant scattering data
| Autor: | C L'abbe, Bart Laenens, Dirk Smeets, Jelle Demeulemeester, Johannes Meersschaut, Nikie Planckaert, André Vantomme, Kristiaan Temst |
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| Rok vydání: | 2009 |
| Předmět: |
Chromium
Diffraction Nuclear and High Energy Physics Computer science Iron Reliability (computer networking) Computer Science::Neural and Evolutionary Computation Phase (waves) Field (computer science) Pattern Recognition Automated law.invention Nuclear magnetic resonance X-Ray Diffraction law Materials Testing Scattering Radiation Instrumentation Radiation Artificial neural network business.industry X-Rays Automation Resonant scattering Synchrotron Neural Networks Computer business Biological system Algorithms Synchrotrons |
| Zdroj: | Journal of Synchrotron Radiation. 17:86-92 |
| ISSN: | 0909-0495 |
| DOI: | 10.1107/s0909049509042824 |
| Popis: | The capabilities of artificial neural networks (ANNs) have been investigated for the analysis of nuclear resonant scattering (NRS) data obtained at a synchrotron source. The major advantage of ANNs over conventional analysis methods is that, after an initial training phase, the analysis is fully automatic and practically instantaneous, which allows for a direct intervention of the experimentalist on-site. This is particularly interesting for NRS experiments, where large amounts of data are obtained in very short time intervals and where the conventional analysis method may become quite time-consuming and complicated. To test the capability of ANNs for the automation of the NRS data analysis, a neural network was trained and applied to the specific case of an Fe/Cr multilayer. It was shown how the hyperfine field parameters of the system could be extracted from the experimental NRS spectra. The reliability and accuracy of the ANN was verified by comparing the output of the network with the results obtained by conventional data analysis. |
| Databáze: | OpenAIRE |
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