Automated Isotope Identification Algorithm Using Artificial Neural Networks

• Published in: IEEE transactions on nuclear science Vol. 64; no. 7; pp. 1858 - 1864
• Main Authors: Kamuda, M., Stinnett, J., Sullivan, C. J.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; artificial neural network; Artificial neural network (ANN); Artificial neural networks; automated isotope identification; Computer simulation; Gamma rays; gamma-ray spectroscopy; Isotopes; Learning algorithms; Learning theory; Libraries; Machine learning; Machine learning algorithms; Mathematical model; Neural networks; Neurons; Pattern recognition; Radioisotopes; Spectra; Training
• ISSN: 0018-9499; 1558-1578; 1558-1578
• DOI: 10.1109/TNS.2017.2693152

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.