Quantitative mobile gamma-ray spectrometry through Bayesian inference

Abstract

Accurate quantitative mapping of gamma-ray sources is critical for applications ranging from radiological emergency response and environmental monitoring to nuclear security and deep space exploration. Here, we show that integrating high-fidelity, platform-dynamic Monte Carlo simulations and Bayesian inference with mobile gamma-ray spectrometry enables rapid and accurate inference of the source mixture, associated source activities, and source locations for both distributed and point-like gamma-ray sources. Validated against laboratory and field assays, our framework quantifies anthropogenic gamma-ray sources that conventional methods cannot resolve in $1\,$s with $\sim\!\!1\,\%$ error. 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.