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Application of neural networks for the analysis of gamma-ray spectra measured with a Ge spectrometer
- Source :
-
Nuclear Instruments & Methods in Physics Research Section A . May2002, Vol. 484 Issue 1-3, p557. 7p. - Publication Year :
- 2002
-
Abstract
- The analysis of gamma-ray spectra to identify lines and their intensities usually requires expert knowledge and time-consuming calculations with complex fitting functions. A neural network algorithm can be applied to a gamma-ray spectral analysis owing to its excellent pattern recognition characteristics. However, a gamma-ray spectrum typically having 4096 channels is too large as a typical input data size for a neural network. We show that by applying a suitable peak search procedure, gamma-ray data can be reduced to peak energy data, which can be easily managed as input by neural networks. The method was applied to the analysis of gamma-ray spectra composed of mixed radioisotopes and the spectra of uranium ores. Radioisotope identification was successfully achieved. [Copyright &y& Elsevier]
- Subjects :
- *RADIOISOTOPES
*GAMMA ray spectrometry
Subjects
Details
- Language :
- English
- ISSN :
- 01689002
- Volume :
- 484
- Issue :
- 1-3
- Database :
- Academic Search Index
- Journal :
- Nuclear Instruments & Methods in Physics Research Section A
- Publication Type :
- Academic Journal
- Accession number :
- 7807851
- Full Text :
- https://doi.org/10.1016/S0168-9002(01)01962-3