Library correlation nuclide identification algorithm

Ruß, W.

doi:10.1016/j.nima.2007.04.062

Cited by 14 publications

(5 citation statements)

References 5 publications

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“…In [27], MLR was firstly proposed as a standalone tool, while in [28] was combined with fuzzy logic [28]. Other works proposed the use of MLR with a correlation measure to identify nuclide patterns as discussed in [29], while the use of MLR together with chi-square-based objectives in radionuclide detection was introduced in [30].…”

Section: Spectrum Analysismentioning

confidence: 99%

Survey of Machine Learning Approaches in Radiation Data Analytics Pertained to Nuclear Security

Alamaniotis

Heifetz

2021

Learning and Analytics in Intelligent Systems

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The increasing concerns over the use of nuclear materials for malevolent purposes (i.e., terrorist attacks) have fueled the interest in developing technologies that can detect hidden nuclear material before its use. The process of detecting and identifying nuclear materials for non-reported purposes is under the umbrella of nuclear security. Among several areas that contribute to the security and safeguards of nuclear materials, radiation data analytics has recently been marked as an area of high potential. Therefore, there is an increasing trend in applying machine learning for developing data analysis methods applied to radiation signals aiming at identifying patterns of interest associated with nuclear materials. This chapter aspires in providing a comprehensive survey and discussion of machine learning and data analytics methods pertained to nuclear security. The chapter will also discuss further trends and how data analytics can further enhance nuclear security by effectively analyzing radiation data.

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Section: Spectrum Analysismentioning

confidence: 99%

Survey of Machine Learning Approaches in Radiation Data Analytics Pertained to Nuclear Security

Alamaniotis

Heifetz

2021

Learning and Analytics in Intelligent Systems

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“…Whenever a peak detection algorithm fails to resolve overlapping peaks, some information will still be extracted from the resulting combined peaks. Therefore, library comparison RIIDs [1][2][3][4][5][6][7][8][9][10] have the potential to work better with customized libraries with a single peak energy and peak area assigned to a composite peak. For example, the libraries in [31] only save information regarding the combined peak.…”

Section: Peak Location Algorithmsmentioning

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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“…The LibCorNID approach is somewhat unusual in that it includes both peak fitting and template matching with tolerances for variations in the calibrations [29]. Its key steps are:…”

Section: Libcornidmentioning

confidence: 99%

Radio-Isotope Identification Algorithms for NaI γ Spectra

Burr

Hamada

2009

Algorithms

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The performance of Radio-Isotope Identification (RIID) algorithms using NaI-based γ spectroscopy is increasingly important. For example, sensors at locations that screen for illicit nuclear material rely on isotope identification using NaI detectors to distinguish innocent nuisance alarms, arising from naturally occurring radioactive material, from alarms arising from threat isotopes. Recent data collections for RIID testing consist of repeat measurements for each of several measurement scenarios to test RIID algorithms. It is anticipated that vendors can modify their algorithms on the basis of performance on chosen measurement scenarios and then test modified algorithms on data for other measurement scenarios. It is therefore timely to review the current status of RIID algorithms on NaI detectors. This review describes γ spectra from NaI detectors, measurement issues and challenges, current RIID algorithms, data preprocessing steps, the role and current quality of synthetic spectra, and opportunities for improvements

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Library correlation nuclide identification algorithm

Cited by 14 publications

References 5 publications

Survey of Machine Learning Approaches in Radiation Data Analytics Pertained to Nuclear Security

Survey of Machine Learning Approaches in Radiation Data Analytics Pertained to Nuclear Security

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

Radio-Isotope Identification Algorithms for NaI γ Spectra

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