Title: Artificial neural networks applied to the analysis of synchrotron nuclear resonant scattering data
Authors: Planckaert, N ×
Demeulemeester, Jelle
Laenens, B
Smeets, Dirk
Meersschaut, J
L'abbe, C
Temst, Kristiaan
Vantomme, André #
Issue Date: Jan-2010
Publisher: Wiley-blackwell publishing, inc
Series Title: Journal of synchrotron radiation vol:17 pages:86-92
Abstract: 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. (C) 2010 International Union of Crystallography Printed in Singapore - all rights reserved
ISSN: 1600-5775
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Nuclear and Radiation Physics Section
ESAT - PSI, Processing Speech and Images
× corresponding author
# (joint) last author

Files in This Item:

There are no files associated with this item.

Request a copy


All items in Lirias are protected by copyright, with all rights reserved.

© Web of science