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