Quantitative mobile gamma-ray spectrometry through Bayesian inference

TL;DR

This paper introduces a Bayesian inference framework combined with Monte Carlo simulations for rapid, accurate quantitative gamma-ray source mapping using mobile spectrometry, significantly improving resolution and speed.

Contribution

It presents a novel integration of high-fidelity simulations and Bayesian methods for real-time gamma-ray quantification, surpassing conventional approaches.

Findings

Achieves 1-second resolution with ~1% error in source quantification.

Validated against laboratory and field data.

Enables applications in radiological safety, geophysical mapping, and space exploration.

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 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 $\sim\!\!1\,\%$ 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.