Quantitative mobile gamma-ray spectrometry through Bayesian inference.

Full-spectrum Bayesian inference with mobile gamma-ray spectrometry and high-fidelity Monte Carlo simulations enables decisive source mixture identification (>5σ) and meter-scale localization from 6 s single-pass data.

• Why it matters: Quantitative mapping of gamma-ray emitters is needed for radiological emergency response, environmental monitoring, nuclear security, and deep space exploration, but accurate recovery from mobile measurements remains challenging.
• What they did: The approach combines mobile gamma-ray spectrometry with high-fidelity Monte Carlo simulations and full-spectrum Bayesian inference, benchmarked using 6 s single-pass data against independent in-situ and laboratory assays.
• The result: Source activities are recovered with percent-level accuracy, enabling improved radiological situational awareness and more robust constraints on radionuclide abundances on extraterrestrial bodies across the Solar System.