Real-Time Classification of Radiation Pulses with Piled-Up Recovery Using an FPGA-Based Artificial Neural Network

Abstract

Artificial neural networks (ANNs) have shown several benefits over the traditional classification methods for radiation detector data, such as greater accuracy and the ability to classify neutron-photon combinations from piled-up events. These capabilities are of particular interest in applications involving intense radiation environments where large instantaneous detector count rates can lead to many piled-up events and subsequent information loss. The recovery of individual radiation detector pulses from piled-up data can improve the efficiency of classification systems, making them more attractive for field applications. This work extends the use of ANN systems with piled-up recovery to the real-time domain, with a focus on the hardware implementation. The ANN system is implemented on a Virtex-5 XC5VSX95T FPGA which collects pulse data at 250MHz and then processes and classifies pulses in the pipelined ANN at lower frequencies. The system's performance is demonstrated by classifying pulses in real-time in a variety of scenarios including passive background, passive plutonium-beryllium (PuBe), and active PuBe. The results show that the system can provide accurate classifications in real-time while displaying the results clearly to the user. The system is shown to be capable of classifying pulses at a maximum rate of 1.11× 106 pulses per second, with a maximum latency of 7.7μs , and an overall accuracy of 98.2%.