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Compton Edge Convolutional Model and Algorithm for Energy-channel Calibration

Yanbiao Zhang, Fanjie Zeng, Dehua Kong, Lian Lei, Zhonghai Wang

TL;DR

This work tackles the challenge of calibrating scintillation detectors when full-energy peaks are weak or absent by introducing a convolution-based method to fit the Compton edge. The approach models the measured spectrum as a convolution of the true spectrum with a Gaussian-like resolution function, linking the Compton-edge energy $E_{e,\max}$ to the observed channel by extracting parameters $B$, $A$, and $σ$ from the fit. Using BC408, NaI, and LaBr$_3$ detectors and ${}^{137}$Cs sources, the authors validate that the Compton-edge calibration agrees with full-energy-peak calibrations to within $1\%$, demonstrating broad applicability across detector materials. The method relies on the Klein–Nishina differential cross section and the resulting recoil-electron energy distribution, enabling automated, universal calibration without reliance on prominent full-energy peaks. This could significantly improve radiation-detection precision in dosimetry, environmental monitoring, and medical imaging by providing a robust alternative calibration pathway for Compton-dominated spectra.

Abstract

Scintillation detectors are essential tools for radiation measurement, but calibrating them accurately can be challenging, especially when full-energy peaks are not prominent. This is common in detectors like plastic scintillators. Current methods for calibrating these detectors often require manual adjustments. To address this, we propose a new method called the convolution model. This model accurately calibrates the energy-channel relationship of the Compton edge in various detectors. We tested it with plastic scintillator BC408, NaI crystal, and LaBr$_3$ crystal. Using ${}^{137}$Cs radioactive sources, we calibrated NaI and LaBr$_3$ detectors using full-energy peaks, then applied the convolution model to fit the Compton edge. Our results show errors within 1\% when compared to full-energy peak calibration.

Compton Edge Convolutional Model and Algorithm for Energy-channel Calibration

TL;DR

This work tackles the challenge of calibrating scintillation detectors when full-energy peaks are weak or absent by introducing a convolution-based method to fit the Compton edge. The approach models the measured spectrum as a convolution of the true spectrum with a Gaussian-like resolution function, linking the Compton-edge energy to the observed channel by extracting parameters , , and from the fit. Using BC408, NaI, and LaBr detectors and Cs sources, the authors validate that the Compton-edge calibration agrees with full-energy-peak calibrations to within , demonstrating broad applicability across detector materials. The method relies on the Klein–Nishina differential cross section and the resulting recoil-electron energy distribution, enabling automated, universal calibration without reliance on prominent full-energy peaks. This could significantly improve radiation-detection precision in dosimetry, environmental monitoring, and medical imaging by providing a robust alternative calibration pathway for Compton-dominated spectra.

Abstract

Scintillation detectors are essential tools for radiation measurement, but calibrating them accurately can be challenging, especially when full-energy peaks are not prominent. This is common in detectors like plastic scintillators. Current methods for calibrating these detectors often require manual adjustments. To address this, we propose a new method called the convolution model. This model accurately calibrates the energy-channel relationship of the Compton edge in various detectors. We tested it with plastic scintillator BC408, NaI crystal, and LaBr crystal. Using Cs radioactive sources, we calibrated NaI and LaBr detectors using full-energy peaks, then applied the convolution model to fit the Compton edge. Our results show errors within 1\% when compared to full-energy peak calibration.
Paper Structure (7 sections, 14 equations, 8 figures, 1 table)

This paper contains 7 sections, 14 equations, 8 figures, 1 table.

Figures (8)

  • Figure 1: Schematic diagram of the Compton effect
  • Figure 2: Differential Cross Section for Recoil Electron Energy
  • Figure 3: convolution process
  • Figure 4: Differential Cross Section for Recoil Electron Energy
  • Figure 5: Compton Edge Fitting
  • ...and 3 more figures