| Item type |
一般雑誌記事 / Article(1) |
| 公開日 |
2023-03-15 |
| タイトル |
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タイトル |
Application of neural networks for the analysis of gamma-ray spectra measured with a Ge spectrometer |
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言語 |
en |
| 言語 |
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|
言語 |
eng |
| キーワード |
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|
主題Scheme |
Other |
|
主題 |
Gamma-ray spectrometr |
| キーワード |
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主題Scheme |
Other |
|
主題 |
Neural network |
| キーワード |
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主題Scheme |
Other |
|
主題 |
Radioisotope identification |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
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資源タイプ |
article |
| 著者 |
Yoshida, Eiji
Shizuma, Kiyoshi
Endo, Satoru
Oka, Takamitsu
Yoshida, Eiji
Shizuma, Kiyoshi
Endo, Satoru
Oka, Takamitsu
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| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
The analysis of gamma-ray spectra to identify lines and their intensities usually requires expert knowledge and timeconsuming 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. |
| 書誌情報 |
Nuclear Instruments & Methods in Physics Research Section A
巻 484,
p. 557-563,
発行日 2002-05
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| フォーマット |
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内容記述タイプ |
Other |
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内容記述 |
application/pdf |
oka_takamitsu_020542.pdf (507.6 kB)
