Classification of Radionuclides on Polyvinyl Toluene Radiation Portal Monitors by a Neural Network Based System§

Abstract

To detect radiation sources in containers, radiation portal monitors (RPMs) are normally used. Among the various types of RPM, polyvinyl toluene (PVT) monitors are the most widely used, thanks to their lower cost. However, they provide only low resolution when energy spectra are measured. For this reason, it is currently not possible to identify what kind of radiation source is being measured, only that there is some radiation source present, when an alarm is triggered in a PVT monitor. In this case, a second inspection is necessary in order to distinguish between innocent alarms and those due to potentially dangerous radioactive material. This second inspection involves a considerable outlay of money and time at frontiers. The main purpose of this paper is to propose an algorithm which can be applied so as to reduce the number of innocent alarms purely on the basis of the data obtained by the PVT monitors. To this end, an intelligent system based on two stages is proposed: a preprocessing of the energy spectrum and a neural network based pattern recognition module, which classifies the radiation source into one of the standard groups. This intelligent system has been tested with real radiation sources, whose energy spectra were measured with two different PVT detectors, yielding good results.