A Neuromorphic Algorithm for Radiation Anomaly Detection

In this work, we present initial results on the development of a neuromorphic spiking neural network for performing gamma-ray radiation anomaly detection, the first known application of neuromorphic computing to be applied to the radiation detection domain. Neuromorphic computing seeks to enable future autonomous systems to obtain machine learning-level performance without the typical high power consumption needs. The detection of anomalous radioactive sources in an urban environment is challenging, largely due to the highly dynamic nature of background radiation. For this evaluation, the spiking neural network is trained and evaluated on the Urban Source Search challenge dataset, a synthetic dataset whose development was funded through the United States Department of Energy. The network's weights and architecture are trained using an evolutionary optimization approach. A preliminary performance evaluation of the spiking neural network indicates significant improvements in source detection sensitivity when compared to an established gross count rate-based algorithm, while meeting ANSI standards for false alarm rate. The SNN achieved half the sensitivity of a different, more complex spectral analysis algorithm from literature, leaving room for future research and development.