A marine radioisotope gamma-ray spectrum analysis method based on Monte Carlo simulation and MLP neural network

TL;DR

This paper introduces a machine learning approach using MLP neural networks combined with Monte Carlo simulation to improve the accuracy of Cs-137 gamma-ray spectrum analysis in seawater, especially under low concentration and poor statistics.

Contribution

It presents a novel MLP-based method that reduces the need for extensive standard sample measurements by training on simulated and measured background spectra.

Findings

MLP method achieves lower RMSE than traditional NPA method

The approach improves precision in Cs-137 concentration estimation

Validated with Geant4 simulations and real seawater spectra

Abstract

The monitoring of Cs-137 in seawater using scintillation detector relies on the spectrum analysis method to extract the Cs-137 concentration. And when in poor statistic situation, the calculation result of the traditional net peak area (NPA) method has a large uncertainty. We present a machine learning based method to better analyze the gamma-ray spectrum with low Cs-137 concentration. We apply multilayer perceptron (MLP) to analyze the 662 keV full energy peak of Cs-137 in the seawater spectrum. And the MLP can be trained with a few measured background spectrums by combining the simulated Cs-137 signal with measured background spectrums. Thus, it can save the time of preparing and measuring the standard samples for generating the training dataset. To validate the MLP-based method, we use Geant4 and background gamma-ray spectrums measured by a seaborne monitoring device to generate an independent test dataset to test the result by our method and the traditional NPA method. We find that the MLP-based method achieves a root mean squared error of 0.159, 2.3 times lower than that of the traditional net peak area method, indicating the MLP-based method improves the precision of Cs-137 concentration calculation