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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Bibliographic Details
Published inJournal of Advanced Simulation in Science and Engineering Vol. 12; no. 1; pp. 233 - 248
Main Authors Suzuki, Kenji, Takase, Tsugiko, Kumada, Yuka, Matsumoto, Masaharu, Yamaguchi, Katsuhiko
Format Journal Article
LanguageEnglish
Published Japan Society for Simulation Technology 2025
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ISSN2188-5303
2188-5303
DOI10.15748/jasse.12.233

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Summary: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.
ISSN:2188-5303
2188-5303
DOI:10.15748/jasse.12.233