Artificial neural network based isotopic analysis of airborne radioactivity measurement for radiological incident detection.

Artificial neural network based isotopic analysis of airborne radioactivity measurement for radiological incident detection. Appl Radiat Isot. 2020 Nov;165:109304 Authors: Woldegiorgis S, Grimes T, Simpson C, Myjak M Abstract Responders need tools to rapidly detect and identify airborne alpha radioactivity during consequence management scenarios. Traditional continuous air monitoring systems used for this purpose compute the net counts in various energy windows to determine the presence of specified isotopes, such as 235U, 239Pu, and 241Am. These calculations rely on having a well-calibrated detector, which is challenging in low-background environments. Here, an alternative approach of using artificial neural networks to classify alpha spectra is presented. Two network architectures, fully connected and convolutional neural networks (CNNs), were trained to classify alpha spectra into four categories: background and background plus the three isotopes above. Sources were injected into measured background at various fractions of the derived response level (DRL) corresponding to early-phase Protective Action Guides. The convolutional network identifies all sources at 1% of the DRL with average probability of detection of 95% and false alarm probability of 1%. Further, the network identifies sources ranging between 0.25% and 1% of the DRL with higher than 80% probability of detection and lower than 7% false alarm probability. Most signifi...
Source: Applied Radiation and Isotopes - Category: Radiology Authors: Tags: Appl Radiat Isot Source Type: research