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Automatic calibration of gamma-ray detectors deployed in uncontrolled environments

Marco Salathe, Nicolas Abgrall, Mark S. Bandstra, Tenzing H. Y. Joshi, Brian J. Quiter, Reynold J. Cooper

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.

Automatic calibration of gamma-ray detectors deployed in uncontrolled environments

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.
Paper Structure (14 sections, 8 equations, 8 figures, 1 table)

This paper contains 14 sections, 8 equations, 8 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methods
  3. Detector calibration model
  4. Energy resolution model
  5. Modeling of naturally occurring radioactive materials
  6. Data modeling
  7. Parameter optimization
  8. Continuous real-time calibration
  9. Results
  10. Simulations
  11. Measurements in an environmental chamber
  12. Performance in the field
  13. Discussion
  14. Conclusion

Figures (8)

  • Figure 1: Normalized light yield non-proportionality curves for NaI(Tl) as a function of deposited energy (logarithmic scale). Curves derived from or attributed to Engelkemeir Engelkemeir1956, Valentine et al. Valentine1998, Rooney et al. Rooney1997, and Dorenbos et al. Dorenbos1995 are compared. The "Ours" curve represents a spline fit through various models. Note the characteristic structures near the iodine K and L edges.
  • Figure 2: Steps involved in calibrating a single spectrum: (A) The histogram (grey) and the different background components (colors) in their unaltered form. (B) The components are "smeared" to match the energy resolution of the detector. (C) The components are squeezed to match the spectrum along X. (D) The amplitude of the components is adjusted (in Y) to sum up to the spectrum.
  • Figure 3: The observed mean bias of the optimization parameters for different integration times, $E_{min}$ and variable vs fixed offset. It is expressed as a percentage deviation from the ground truth average (7.5% for the energy resolution, 2.2 for the cosmic power law, 0.275 for the gain, $2\times10^{-5}$ for the saturation and 2 keV for the offset). The data for fixed offset lie all on top of each other and thus are barely distinguishable.
  • Figure 4: Same as Figure \ref{['fig:sim_bias']}, but for the normalized standard deviations describing the spreads of the fitted parameters. It is expressed as a percentage spread normalized by the ground truth average. The data for fixed offset lie all on top of each other and thus are barely distinguishable.
  • Figure 5: The distribution of the loss function (reduced deviance) for various dwell times: The colored shapes are from simulated spectra (the width of the bands indicates differences between various low-energy thresholds), the dashed lines are from the fixed-parameter fit (the statistical expected distribution under perfect circumstances). The vertical dotted lines mark the mean of each distribution.
  • ...and 3 more figures