Artificial Neural Network for Unfolding Accelerator-based Neutron Spectrum by Means of Multiple-foil Activation Method

T. Kin, Y. Sanzen, M. Kamida, K. Aoki, N. Araki, Y. Watanabe

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

3 被引用数 (Scopus)

抄録

In medical radioisotope (RI) production by accelerator neutron, double-differential thick-target neutron yield (DDTTNY) is necessary to be measured to estimate production amount and its radioactive and isotopic purity. We adopted the multiple-foil activation method for the measurement. The DDTTNY should be derived by an unfolding technique from measured numbers of produced atoms via the activation reactions. We have developed an unfolding code using artificial neural network (ANN) which requires no initial guess spectrum and no human-inducible convergence condition which are required for conventional unfolding methods. To demonstrate the ability to derive DDTTNY by the ANN unfolding code, we input numbers of produced atoms obtained by a multiple-foil activation experiment conducted at Kyushu University Tandem Laboratory. The resultant DDTTNY is compared with that by GRAVEL code, which is one of the conventional codes. Since there is no large discrepancy, we found that the ANN unfolding code has same ability to GRAVEL code even no initial guess spectrum was used.

本文言語英語
ホスト出版物のタイトル2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 - Conference Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781538622827
DOI
出版ステータス出版済み - 11月 12 2018
イベント2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 - Atlanta, 米国
継続期間: 10月 21 201710月 28 2017

出版物シリーズ

名前2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 - Conference Proceedings

その他

その他2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017
国/地域米国
CityAtlanta
Period10/21/1710/28/17

!!!All Science Journal Classification (ASJC) codes

  • 器械工学
  • 放射線学、核医学およびイメージング
  • 核物理学および高エネルギー物理学

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