Automated Isotope Identification Algorithm Using Artificial Neural Networks

Abstract

There is a need to develop an algorithm that can determine the relative activities of radioisotopes in a large data set of low-resolution gamma-ray spectra that contain a mixture of many radioisotopes. Low-resolution gamma-ray spectra that contain mixtures of radioisotopes often exhibit feature overlap, requiring algorithms that can analyze these features when overlap occurs. While machine learning and pattern recognition algorithms have shown promise for the problem of radioisotope identification, their ability to identify and quantify mixtures of radioisotopes has not been studied. Because machine-learning algorithms use abstract features of the spectrum, such as the shape of overlapping peaks and Compton continuum, they are a natural choice for analyzing radioisotope mixtures. An artificial neural network (ANN) has been trained to calculate the relative activities of 32 radioisotopes in a spectrum. The ANN is trained with simulated gamma-ray spectra, allowing easy expansion of the library of target radioisotopes. In this paper, we present our initial algorithms based on an ANN and evaluate them against a series of measured and simulated spectra.