Isotope Identification Using Artificial Neural Network Ensembles and Bin-Ratios

Abstract

Reliable, automatic isotope identification is crucial for improving the performance of radioactivity monitoring, especially in security applications. In this article, a robust identification method based on an ensemble employing artificial neural networks (ANN) was developed and compared with other popular machine learning methods. We have encoded the histogram features using bin-ratio vectors that increase the classification accuracy. To make experimentation more objective, our datasets are generated from real isotope spectra of Cs <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> LiLaBr <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> (Ce) (CLLBC) detectors using realistic background and gain shift noise profiles, based on the requirements of American National Standards Institute (ANSI) N42.34. In addition to experimenting with classifying individual isotopes we also evaluate the detection of isotope mixtures, where the proposed method also performs competitively.