Application of neural networks for the analysis of gamma-ray spectra measured with a Ge spectrometer

Yoshida, Eiji; Shizuma, Kiyoshi; Endo, Satoru; Oka, T.

doi:10.1016/s0168-9002(01)01962-3

Cited by 104 publications

(34 citation statements)

References 9 publications

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“…For problems where isotopes of interest are difficult to acquire for academic research, such as isotopes important to nuclear forensics, simulated gamma-ray spectra can be used to train an ANN. While some work has been done applying machine learning algorithms to spectroscopy problems, the topic of simulated training data and an ANNs ability to perform identification and quantification on mixtures of isotopes using low-resolution detectors has not been sufficiently explored [2,3,4]. An ANN tailored to perform isotope identification and quantification on mixtures of isotopes using low-resolution gamma-ray spectra has the potential to operate with minimal human intervention.…”

Section: 23 210 Two Sampledmentioning

confidence: 99%

“…ANNs have been applied to peak fitting [31], isotope identification [2,3], and activity estimation [2,32]. Many of this work rely on ROI methods [33], feature extraction [34], high-resolution gamma-ray spectra as the input to the ANN [4], small libraries of isotopes, and assume perfectly calibrated detectors. ANN training methods created for high-resolution gamma-ray spectra may not perform well when trained using low-resolution spectra given the large discrepancy in resolution.…”

Section: Existing Neural Network Applications To Isotope Identificatimentioning

confidence: 99%

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Automated Isotope Identification Algorithm Using Artificial Neural Networks

Kamuda

Stinnett

Sullivan

2017

IEEE Trans. Nucl. Sci.

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There is a need to develop an algorithm that can determine the relative activities of a mixture of many isotopes in a low-resolution gamma-ray spectrum. While techniques for this task exist, they require a human operator and are too slow to use on very large datasets of spectra. Pattern recognition algorithms such as neural networks are prime candidates for automated isotope identification using low-resolution gamma-ray spectra. While algorithms based on feature extraction such as peak finding or ROI algorithms work well for well calibrated high resolution detectors, for low-resolution detectors it may be more beneficial to use algorithms that incorporate more abstract features of the spectrum. This is especially true when analyzing a mixture of isotopes where peak overlap and Compton continuum effects occlude features of interest. To solve this, an artificial neural network (ANN) was trained to predict the presence and relative activities of isotopes from a mixture of many isotopes. The

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Section: 23 210 Two Sampledmentioning

confidence: 99%

Section: Existing Neural Network Applications To Isotope Identificatimentioning

confidence: 99%

Automated Isotope Identification Algorithm Using Artificial Neural Networks

Kamuda

Stinnett

Sullivan

2017

IEEE Trans. Nucl. Sci.

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“…More recently, Kangas et al reported the results of applying multilayer perceptron neural networks in [4] to analyze the shape of low resolution polyvinyl toluene spectra data acquired from port monitoring technology. Multilayer perceptrons were also applied by Vignerson et al, in [5], to determine 235 U/U total ratios, and Yoshida et al for radionuclide detection in uranium ore [6].…”

Section: A Related Workmentioning

confidence: 99%

Anomaly detection in gamma ray spectra: A machine learning perspective

Sharma

Bellinger

Japkowicz

et al. 2012

2012 IEEE Symposium on Computational Intelligence for Security and Defence Applications

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Abstract-With Canadian security and the safety of the general public in mind, physicists at Health Canada (HC) have begun to develop techniques to identify persons concealing radioactive material that may represent a threat to attendees at public gatherings, such as political proceedings and sporting events. To this end, Health Canada has initiated field trials that include the deployment of gamma-ray spectrometers. In particular, a series of these detectors, which take measurements every minute and produce 1,024 channel gamma-ray spectrum, were deployed during the Vancouver 2010 olympics. Simple computerized statistics and human expertise were used as the primary line of defence. More specifically, if a measured spectrum deviated significantly from the background, an internal alarm was sounded and an HC physicist undertook further analysis into the nature of the alarming spectrum. This strategy, however, lead to a significant number of costly and time consuming false positives. This research applies sophisticated machine learning algorithms to reduce the number of false positives to an acceptable level, the results of which are detailed in this paper. In addition, we emphasize the primary findings of our work and highlight avenues available to further improve upon our current results.

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“…ANN eliminates the limitations of classical approaches by extracting the desired information from the input data. Applying ANN to a spectrometry system needs sufficient input and output data instead of mathematical equations for performing the fit to nuclear spectra, including X-, gamma-ray and alpha-particles spectra (Baeza et al, 2011;Basheer and Hajmeer, 2000;Keller et al, 1995;Yoshida et al, 2002;Kangas et al, 2008;Chen and Wei, 2009;Medhat, 2012;Miranda et al, 2009;Doostmohammadi et al, 2010). For each nuclear spectrum, such as alpha spectrum, up to 2048 data points are selected as inputs.…”

Section: Introductionmentioning

confidence: 99%