Quantitative mobile gamma-ray spectrometry through Bayesian inference

Abstract

Accurate quantitative mapping of gamma-ray emitters is critical for applications ranging from radiological emergency response and environmental monitoring to nuclear security and deep space exploration. Here, we show that such mapping can be achieved by combining mobile gamma-ray spectrometry with high-fidelity Monte Carlo simulations and full-spectrum Bayesian inference. Using 6 s of single-pass mobile spectrometry data benchmarked against independent in-situ and laboratory assays, we demonstrate decisive source mixture identification ($>\!\!5\sigma$), meter-scale source localization, and recovery of source activities with percent-level accuracy. The developed method marks a critical advance in quantitative gamma-ray sensing, enabling improved radiological situational awareness, enhanced terrestrial geophysical and geochemical mapping, as well as more robust constraints on radionuclide abundances on extraterrestrial bodies across the Solar System.