Estimating radiation source distribution from measured γ-ray energy spectra
Using machine learning, a method was developed to estimate the distribution of radiation sources arranged on a two-dimensional plane with high accuracy from the measured γ-ray energy spectra. A new machine learning method was used to construct a Spectrum Renormalization Filter (SRF) to convert the e...
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| Published in | Journal of Advanced Simulation in Science and Engineering Vol. 12; no. 1; pp. 233 - 248 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Japan Society for Simulation Technology
2025
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2188-5303 2188-5303 |
| DOI | 10.15748/jasse.12.233 |
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| Abstract | Using machine learning, a method was developed to estimate the distribution of radiation sources arranged on a two-dimensional plane with high accuracy from the measured γ-ray energy spectra. A new machine learning method was used to construct a Spectrum Renormalization Filter (SRF) to convert the experimental data to be verified into a spectrum close to the shape of a simulation. The method using SRF produced more accurate estimation results than a method improving the accuracy of the simulation data used for training the estimation of the radiation sources distribution. |
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| AbstractList | Using machine learning, a method was developed to estimate the distribution of radiation sources arranged on a two-dimensional plane with high accuracy from the measured γ-ray energy spectra. A new machine learning method was used to construct a Spectrum Renormalization Filter (SRF) to convert the experimental data to be verified into a spectrum close to the shape of a simulation. The method using SRF produced more accurate estimation results than a method improving the accuracy of the simulation data used for training the estimation of the radiation sources distribution. |
| Author | Kumada, Yuka Suzuki, Kenji Yamaguchi, Katsuhiko Matsumoto, Masaharu Takase, Tsugiko |
| Author_xml | – sequence: 1 fullname: Suzuki, Kenji organization: Faculty of Symbiotic Systems Science, Fukushima University – sequence: 1 fullname: Takase, Tsugiko organization: Faculty of Symbiotic Systems Science, Fukushima University – sequence: 1 fullname: Kumada, Yuka organization: Faculty of Symbiotic Systems Science, Fukushima University – sequence: 1 fullname: Matsumoto, Masaharu organization: Information Technology Center, Fukushima University – sequence: 1 fullname: Yamaguchi, Katsuhiko organization: Faculty of Symbiotic Systems Science, Fukushima University |
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| Cites_doi | 10.15669/pnst.4.76 10.15748/jasse.7.71 10.1016/j.anucene.2018.09.022 10.1002/9783527805600 10.1038/s41598-021-81546-4 10.1080/00223131.2023.2275736 |
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| References_xml | – reference: [4] M. Sasaki, Y. Sanada, E. Katengeza, A. Yamamoto: New method for visualizing the dose rate distribution around the Fukushima Daiichi Nuclear Power Plant using artificial neural networks, Scientific Reports, 11 (2021), 1857. – reference: [3] Government of Japan: Events and highlights on the progress related to recovery operations at TEPCO’s Fukushima Daiichi Nuclear Power Station, Ministry of Foreign Affairs of Japan, Tokyo, 2023. – reference: [5] S. Sugaya, T. Endo, Y. Akio: Inverse estimation methods of unknown radioactive source for fuel debris search, Annals of Nuclear Energy, 124 (2019), 49-57. – reference: [2] Y. Sanada, T. Sugita, Y. Nishizawa, A. Kondo, T. Torii: The aerial radiation monitoring in Japan after the Fukushima Daiichi nuclear power plant accident, Progress in Nuclear Science and Technology, 4 (2014), 76-80. – reference: [9] T. Sato, Y. Iwamoto, S. Hashimoto, T. Ogawa, T. Furuta, S. Abe, T. Kai, Y. Matsuya, N. Matsuda, Y. Hirata, T. Sekikawa, L. Yao, P.E. Tsai, H.N. Hunter, H. Iwase, Y. Sakaki, K. Sugihara, N. Shigyo, L. Sihver and K. Niita: Recent improvements of the Particle and Heavy Ion Transport code System - PHITS version 3.33, Journal of Nuclear Science and Technology, 61 (2024), 127-135. – reference: [1] IAEA: The Fukushima Daiichi Accident Technical Volume 1/5 Description and Context of the Accident, Vienna, Austria, 2015. – reference: [7] J. Bogard, D. Downing, R. Coleman, K. Eckerman, J. Turner: Atoms, Radiation, and Radiation Protection fourth edition, Wiley-VCH, Verlag Chemie, 2022. – reference: [8] I. Goodfellow, Y. Bengio A. Courville: DEEP LEARNING, The MIT Press, Cambridge, 2016. – reference: [6] T. Uemura, K. Yamaguchi: Estimation of radiation source distribution using machine learning with γ ray energy spectra, Journal of Advanced Simulation in Science and Engineering, 7 (2020), 71-81. – ident: 3 – ident: 1 – ident: 2 doi: 10.15669/pnst.4.76 – ident: 6 doi: 10.15748/jasse.7.71 – ident: 5 doi: 10.1016/j.anucene.2018.09.022 – ident: 7 doi: 10.1002/9783527805600 – ident: 4 doi: 10.1038/s41598-021-81546-4 – ident: 8 – ident: 9 doi: 10.1080/00223131.2023.2275736 |
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| Title | Estimating radiation source distribution from measured γ-ray energy spectra |
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