Multi-radioisotope identification algorithm using an artificial neural network for plastic gamma spectra

Abstract

Radioisotope identification using a plastic scintillation detector has been a challenging issue because of the poor spectral resolution and low cross-sections of these types of detectors when used for photoelectric absorption. In this paper, we propose an algorithm that identifies a single radioisotope and multiple radioisotopes from the gamma spectrum of a plastic scintillator using an artificial neural network. The spectra were simulated using Monte Carlo N-Particle Transport Code 6 to formulate the training set, and the spectra were measured by a two-inch EJ-200 to create the test set (1440 spectra in total). The ANN-based algorithm presented here ensures an identification accuracy of 98.9% for a single radioisotope and 99.1% for multiple radioisotopes. Even if the spectra were intentionally shifted by 36 keV for low and high energies, the trained ANN predicts radioisotopes with high accuracy. In addition, we have determined the minimal required number of detected counts to identify the radioisotope with 5% false negative and false positive.