A Machine Learning Approach to a Multidetector Array Response Function for Nuclear Search

Abstract

During nuclear search operations, the localization of radioactive sources can be a time-consuming process that requires mapping relative radiation intensity in a large area to determine the position of a source. This article introduces the use of machine learning, specifically a temporal convolutional network (TCN), to estimate the direction between a detector array and a static <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">137</sup> Cs source. This application of machine learning provides a directional vector in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4\pi $ </tex-math></inline-formula> with a 90% confidence of 5.6° and a 99% confidence within 11.2°. With the use of low-cost NaI(Tl) detectors, the effects of self-shielding within the array creates gamma-ray shadows depending on the orientation to the source. Using the convolved detector array response function, we apply supervised machine learning with a neural network to predict a unit vector that points toward the observed source. The directional vector is expected to reduce search times once implemented in future work.