Automatic calibration of gamma-ray detectors deployed in uncontrolled environments

TL;DR

This paper introduces a software-based calibration method for gamma-ray detectors that maintains stable energy calibration in uncontrolled environments without active temperature control, using full-spectrum analysis and physical modeling.

Contribution

The novel calibration technique eliminates the need for temperature stabilization by employing continuous spectral fitting with a Monte Carlo detector model, suitable for large-scale unattended deployments.

Findings

Successfully maintains calibration across -25C to +50C temperature range

Validated with simulated, chamber, and outdoor data

Decouples instrumental drift from environmental spectral changes

Abstract

Radiation detectors deployed as part of a large urban network or for homeland security monitoring must maintain reliable energy calibration even when subjected to substantial variations in temperature and ambient background radiation. Traditional calibration methods often rely on power-intensive temperature stabilization or peak-locking algorithms that are susceptible to environmental changes. This publication presents a novel software-based calibration method that eliminates the need for active temperature control by utilizing full-spectrum analysis. The method continuously updates the calibration parameters by fitting the spectral data with a series of background radiation contributions (K, U, Th series, radon progeny and cosmics) combined with a Monte-Carlo-based physical detector model that incorporates light yield non-proportionality and photomultiplier tube saturation. Performance was validated using simulated data, measurements in an environmental chamber across a wide temperature range (-25C to +50C), and data from a multi-day outdoor field deployment. Results demonstrate that the method successfully maintains stable energy calibration despite significant ambient temperature variations and precipitation events. The technique effectively decouples instrumental drift from spectral changes caused by environmental background fluctuations. This approach provides a robust, automated, and low-power alternative to conventional calibration techniques, enabling the practical deployment of large-scale, unattended networked detector systems.