Application of a Simple, Spiking, Locally Competitive Algorithm to Radionuclide Identification

Many radionuclide identification algorithms use statistical inference to collect a variety of features from gamma-ray spectra to deduce the presence of particular radionuclides. More modern algorithms require large amounts of data to learn and use latent features from spectra for classification. Both approaches are computationally expensive, which is reflected in their power consumption, and require large amounts of user intervention to prepare. In this article, we introduce a low-power, neuromorphic algorithm for the real-time identification of radionuclides which simultaneously considers the entire shape of a gamma-ray spectrum. Utilizing the output of a traditional gamma-ray detector, our spiking, locally competitive algorithm uses sparse coding optimization to compare global patterns in a gamma-ray spectrum with a dictionary of radionuclide templates. This approach allows us to model informative global features resulting from both photoelectric absorption and Compton scattering. For the purpose of radiation threat reduction, the dictionary consists of data from the Nuclear Wallet Cards, a list of radionuclides and their properties compiled by the National Nuclear Data Center. To test our algorithm, we use a variety of gamma-ray spectra created using radionuclides measured under laboratory conditions with varying durations, distances, activity levels, and backgrounds, resulting in a wide range of signal-to-noise ratios. We have created test sets for three different gamma-ray detector types, with ⁵⁷ Co, ¹³⁷ Cs, ¹⁵² Eu, ⁶⁰ Co, ²³⁹ Pu, and ²³⁵ U sources, to quantify the effect of resolution, efficiency, and background on the accuracy of the algorithm. We demonstrate a true positive accuracy of 91% with a high-resolution detector and 89% with a low-resolution detector on the corresponding test sets. Experimenting with the same radionuclides included in the test sets in a variety of special nuclear material (SNM) masking configurations, we show that our algorithm is capable of correctly identifying both SNM and mask even when the activity level of the mask is several times higher than that of the SNM. We also determine that our algorithm achieves over a 99% reduction in power consumption over other radionuclide identification software applications, which is critical for long-term, independent monitoring and is the goal of this research.