A Novel Methodology for Gamma-Ray Spectra Dataset Procurement Over Varying Standoff Distances and Source Activities
Document Type
Article
Publication Date
10-2024
Abstract
The adoption of machine learning approaches for gamma-ray spectroscopy has received considerable attention in the literature. Many studies have investigated the deployment of various algorithm architectures to a specific task. However, little attention has been afforded to the development of the datasets leveraged to train the models. Such training datasets typically span a set of environmental or detector parameters to encompass a problem space of interest to a user. Variations in these measurement parameters will also induce fluctuations in the detector response, including pile-up and ground scatter effects. Fundamental to this work is the understanding that 1) the underlying spectral shape varies as the measurement parameters change and 2) the statistical uncertainties associated with two spectra impact their level of similarity. While previous studies attribute some arbitrary discretization to the measurement parameters for the generation of their synthetic training data, this work introduces a principled methodology for efficient spectral-based discretization of a problem space. A signal-to-noise ration (SNR) respective spectral comparison measure and a Gaussian Process Regression (GPR) mode are used to predict the spectral similarity across a range of measurement parameters. This innovative approach effectively showcased its capability by dividing problem space, ranging from 5cm to 100 cm standoff distances and 5 μCi–100 μCi of Cs, into three unique combinations of measurement parameters. The findings from this work will aid in creating more robust datasets, which incorporate many possible measurement scenarios, reduce the number of required experimental test set measurements, and possibly enable experimental training data collection for gamma-ray spectroscopy.
Source Publication
Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
Recommended Citation
Fjeldsted, A. P., Morrow, T. J., Scott, C., Zhu, Y., Holland, D. E., Hanks, E. M., Lintereur, A. T., & Wolfe, D. E. (2024). A novel methodology for gamma-ray spectra dataset procurement over varying standoff distances and source activities. Nuclear Inst. and Methods in Physics Research, A, 1067. https://doi-org.afit.idm.oclc.org/10.1016/j.nima.2024.169681
Comments
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