Estimating radiation source distribution from measured γ-ray energy spectra

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.