A Datacentric Algorithm for Gamma-ray Radiation Anomaly Detection in Unknown Background Environments

The detection of anomalous radioactive sources in environments characterized by a high level of variation in the background radiation is a challenging problem in nuclear security. A variety of natural and artificial sources contribute to background radiation dynamics including variations in the absolute and relative concentrations of naturally occurring radioisotopes in different materials, the wet-deposition of ²²²Rn daughters during precipitation, and background suppression due to physical objects in the detector scene called ``clutter." This dissertation presents a new datacentric algorithm for radiation anomaly detection in dynamic background environments. The algorithm is based on a custom deep neural autoencoder architecture called the Autoencoder Radiation Anomaly Detection (ARAD) model. An autoencoder is a type of neural network that compresses data at its input through a series of computational layers into a dimensionally-constrained representation called the latent space. It then uses another set of layers to decompress the latent space back into its original dimensionality. When trained on typical background radiation data, ARAD learns an efficient representation of the components comprising typical gamma ray background radiation spectra. If a gamma ray spectrum containing anomalous radiation signatures is presented to ARAD, it is unable to reconstruct the spectrum at its input which can be used to trigger a detection alarm. ARAD was trained and tested on both simulated and real world gamma ray radiation data. Results indicate that ARAD shows a high level of resilience to background dynamics while also displaying good detection performance on a variety of simulated and real world sources.