Quantitative mobile gamma-ray spectrometry through Bayesian inference

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 quantification of distributed and point-like gamma-ray sources. Validated against laboratory and field assays, our framework quantifies natural and anthropogenic gamma-ray sources that conventional methods cannot resolve in $1$s with $\sim \text{\hspace{0.17em}⁣}\text{\hspace{0.17em}⁣}1\mathrm{\%}$ 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.