Interpolation of computed gamma-ray detector response functions

Gamma-ray spectra measured by traditional detectors contain features that result from a combination of the effects of detector materials/geometry, the incident gamma-ray energy, and the angle of entry. The features, such as the full-energy photopeak, Compton continuum, annihilation peak, and escape peaks, are governed by simple relationships depending on incident energy and have been known for a long time. Monte Carlo computer simulations of gamma rays interacting with a detector will show these features, and with a resolution function applied, the results should look similar to real measurements. The traditional approach to creating a detector response function requires many separate simulations of monoenergetic gamma rays striking the detector. This paper presents a new approach to developing computed detector response functions. The new approach involves a much smaller number of monoenergetic gamma-ray simulations and uses interpolation to quickly generate the responses of gamma rays that were not simulated. During the interpolation process, the underlying physics equations are used to accurately compute the response of a given energy gamma ray from the small set of simulations. Such work enables accelerated generation of synthetic radiation detector data. • Key features of the gamma-ray detector response function are listed along with their dependence on the initial energy of the incident gamma ray. • The order of these key features changes as the incident energy increases. • Regions of the response function between key energies changes slowly, allowing for interpolation. • Interpolation requires rebinning, to get the energy bins to line up correctly. • Interpolated response functions match detailed Monte Carlo solutions well.

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Publication Details

Journal
Radiation Physics and Chemistry
Published
2026-01-07
DOI
https://doi.org/10.1016/j.radphyschem.2026.113592
Primary Topic
Radiation Detection and Scintillator Technologies
Type
article
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article

Interpolation of computed gamma-ray detector response functions

Mark S Bandstra, Brian J. Quiter, Andrew Nicholson, Douglas E. Peplow et al.
Radiation Physics and Chemistry
Radiation Detection and Scintillator Technologies
article

Interpolation of computed gamma-ray detector response functions

Mark S Bandstra, Brian J. Quiter, Andrew Nicholson, Douglas E. Peplow, Cihangir Çelik, D. E. Archer, Mathew W. Swinney
article en

Abstract

Gamma-ray spectra measured by traditional detectors contain features that result from a combination of the effects of detector materials/geometry, the incident gamma-ray energy, and the angle of entry. The features, such as the full-energy photopeak, Compton continuum, annihilation peak, and escape peaks, are governed by simple relationships depending on incident energy and have been known for a long time. Monte Carlo computer simulations of gamma rays interacting with a detector will show these features, and with a resolution function applied, the results should look similar to real measurements. The traditional approach to creating a detector response function requires many separate simulations of monoenergetic gamma rays striking the detector. This paper presents a new approach to developing computed detector response functions. The new approach involves a much smaller number of monoenergetic gamma-ray simulations and uses interpolation to quickly generate the responses of gamma rays that were not simulated. During the interpolation process, the underlying physics equations are used to accurately compute the response of a given energy gamma ray from the small set of simulations. Such work enables accelerated generation of synthetic radiation detector data. • Key features of the gamma-ray detector response function are listed along with their dependence on the initial energy of the incident gamma ray. • The order of these key features changes as the incident energy increases. • Regions of the response function between key energies changes slowly, allowing for interpolation. • Interpolation requires rebinning, to get the energy bins to line up correctly. • Interpolated response functions match detailed Monte Carlo solutions well.

Radiation Physics and ChemistryVol. 242
Oak Ridge National Laboratory (US), Lawrence Berkeley National Laboratory (US)
Affordable and clean energy
Openalex Percentile: Top 94%
Radiation Detection and Scintillator Technologies
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