Development and validation of a high-fidelity full-spectrum Monte Carlo model for the Swiss airborne gamma-ray spectrometry system

Abstract

Airborne Gamma-Ray Spectrometry (AGRS) is a critical tool for radiological emergency response, enabling the rapid identification and quantification of hazardous terrestrial radionuclides over large areas. However, existing calibration methods are limited to a few gamma-ray sources, excluding most radionuclides released in severe nuclear accidents and nuclear weapon detonations, compromising effective response and risk assessment. Here, we present a high-fidelity Monte Carlo model that overcomes these limitations, offering full-spectrum calibration for any gamma-ray source. Unlike previous approaches, our model integrates a detailed mass model of the aircraft and a calibrated non-proportional scintillation model, enabling accurate event-by-event predictions of the spectrometer's response to arbitrarily complex gamma-ray fields. Validation in near-, mid-, and far-field scenarios demonstrates that the model not only addresses major deficiencies of previous approaches but also achieves the accuracy required to supersede empirical calibration methods. This advancement enables high-fidelity spectral signature generation for any gamma-ray source, reduces calibration time and costs, minimizes reliance on high-intensity sources, and eliminates related radioactive waste. The approach presented here is a critical step toward integrating advanced full-spectrum data reduction methods for AGRS, unlocking new capabilities beyond emergency response, such as atmospheric cosmic-ray flux quantification for geophysics and trace-level airborne radionuclide identification for nuclear security.