Effects of high count rate and gain shift on isotope identification algorithms

Robinson, Sean M.; Kiff, Scott D.; Ashbaker, Eric D.; Bender, Sarah; Flumerfelt, Eric; Salvitti, Matthew; Borgardt, James D.; Woodring, Mitchell L.

doi:10.1109/nssmic.2007.4437211

Cited by 4 publications

(3 citation statements)

References 2 publications

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“…Other radioisotopes have different gamma birth energies with different relative intensities so in theory, the potential signature of most radioisotopes is at least moderately distinct from that of other isotopes. In practice, for relatively low resolution detector types such as NaI detectors (commonly deployed detector and the main type of interest here, although higher resolution is also discussed), RIID performance can be surprisingly poor [1][2][3][4][5][7][8][9][10][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Application of the Approximate Bayesian Computation Algorithm to Gamma-Ray Spectroscopy

et al. 2020

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Radioisotope identification (RIID) algorithms for gamma-ray spectroscopy aim to infer what isotopes are present and in what amounts in test items. RIID algorithms either use all energy channels in the analysis region or only energy channels in and near identified peaks. Because many RIID algorithms rely on locating peaks and estimating each peak’s net area, peak location and peak area estimation algorithms continue to be developed for gamma-ray spectroscopy. This paper shows that approximate Bayesian computation (ABC) can be effective for peak location and area estimation. Algorithms to locate peaks can be applied to raw or smoothed data, and among several smoothing options, the iterative bias reduction algorithm (IBR) is recommended; the use of IBR with ABC is shown to potentially reduce uncertainty in peak location estimation. Extracted peak locations and areas can then be used as summary statistics in a new ABC-based RIID. ABC allows for easy experimentation with candidate summary statistics such as goodness-of-fit scores and peak areas that are extracted from relatively high dimensional gamma spectra with photopeaks (1024 or more energy channels) consisting of count rates versus energy for a large number of gamma energies.

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Section: Introductionmentioning

confidence: 99%

Application of the Approximate Bayesian Computation Algorithm to Gamma-Ray Spectroscopy

et al. 2020

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“…Spectroscopic gamma-ray detectors are used for many research, industrial, medical and homeland-security applications [1][2][3][4]. The advantages of a digital system for gamma-ray Proceedings of the 8 th ICEENG Conference, 29-31 May, 2012 EE082 -2 spectroscopy in comparison with a classical analog system are reflected in the possibilities of implementation of complex algorithms and simple and rapid modification of algorithms used for signal processing.…”

Section: Introductionmentioning

confidence: 99%

Experimental Comparison among Pileup Recovery Algorithms for Digital Gamma-Ray Spectroscopy

Mahmoud

El_Tokhy

Konber

2012

The International Conference on Electrical Engineering

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This paper presents algorithms for overcoming a common problem of gamma-ray spectroscopy, namely the peak pileup recovery problem. Three different approaches are studied and evaluated within a spectroscopy system. The first approach is a direct search based on Nelder-Mead technique without any derivatives in order to find the local minimum points. A Gaussian shape in conjunction with the peak height and its position of each pulse are used to construct the pulse. Hence, the main pulse parameters such as peak amplitude, position and width can be determined. The second algorithm is based on the nonlinear least square method. In this paper another technique is tried. This technique which is proposed as third algorithm is based on a maximum peak search method combined with the first derivative method to determine peak position of each pulse. Comparison among these approaches is conducted in terms of parameters errors and execution time. This comparison based on a reference signal from 137 Cs point source acquired by data acquisition system. The third approach shows the best accuracy, for determining accurate energy spectrum. Therefore, it provides better isotope identification than other algorithms.

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“…Spectroscopic gamma-ray detectors are used for many research, industrial, medical and homeland-security applications [1][2][3][4]. The advantages of a digital system for gamma ray spectroscopy in comparison with a classical analog system are reflected in the possibilities of implementation of complex algorithms and simple and rapid modification of algorithms used for signal processing.…”

Section: Introductionmentioning

confidence: 99%

Pileup Recovery Algorithms for Digital Gamma Ray Spectroscopy

Mahmoud

El_Tokhy

Konber

2012

The International Conference on Electrical Engineering

View full text Add to dashboard Cite

This paper presents algorithms for overcoming a common problem of gamma ray spectroscopy, namely the peak pileup recovery problem. Three different approaches are studied and evaluated within a spectroscopy system. The algorithms are evaluated by the means of parameters error and fitting error calculations. The first approach is a direct search based on Nelder-Mead technique without any derivatives in order to find the local minimum points. A Gaussian shape in conjunction with the peak height and its position of each pulse are used to construct the pulse. So, the main pulse parameters such as peak amplitude, position and width can be determined. The second algorithm is based on the nonlinear least square method. This approach has accuracy of recovering the original pulses with mean square error of 4.7306x10-12. In this paper another technique is tried. This technique which is proposed as third algorithm is based on a maximum peak search method combined with the first derivative method to determine peak position of each pulse. Comparison among these approaches is conducted in terms of parameters errors. The pulse parameters have been calculated and compared with the actual one. The second approach shows the best accuracy, for determining peak height and position, but the width parameter scored the highest error.

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Effects of high count rate and gain shift on isotope identification algorithms

Cited by 4 publications

References 2 publications

Application of the Approximate Bayesian Computation Algorithm to Gamma-Ray Spectroscopy

Application of the Approximate Bayesian Computation Algorithm to Gamma-Ray Spectroscopy

Experimental Comparison among Pileup Recovery Algorithms for Digital Gamma-Ray Spectroscopy

Pileup Recovery Algorithms for Digital Gamma Ray Spectroscopy

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