A comparison of machine learning methods for automated gamma-ray spectroscopy

Kamuda, Mark; Zhao, Jifu; Huff, Kathryn

doi:10.1016/j.nima.2018.10.063

Title: A comparison of machine learning methods for automated gamma-ray spectroscopy

Full Record
Other Related Research

Abstract

Pattern recognition algorithms such as artificial neural networks (NNs) and convolution neural networks (CNNs) are prime candidates to perform automated gamma-ray spectroscopy. The way these models train and operate mimic how trained spectroscopists identify spectra. These models have shown promise in identifying gamma-ray spectra with large calibration drift and unknown background radiation fields. In this work, two algorithms for mixtures of radioisotopes based on NN and CNN are presented and evaluated.

Authors:

Kamuda, Mark ^[1]; Zhao, Jifu ^[1]; Huff, Kathryn ^[1]

Univ. of Illinois, Urbana-Champaign, IL (United States)

Publication Date:: Mon Oct 01 00:00:00 EDT 2018

Research Org.:: Univ. of Michigan, Ann Arbor, MI (United States)

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:: 1524430

Alternate Identifier(s):: OSTI ID: 1636277

Grant/Contract Number:: NA0002534

Resource Type:: Accepted Manuscript

Journal Name:: Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment

Additional Journal Information:: Journal Volume: 954; Journal ID: ISSN 0168-9002

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 73 NUCLEAR PHYSICS AND RADIATION PHYSICS; Automated Isotope Identification; Neural Networks; Gamma-ray Spectroscopy

Citation Formats


                    Kamuda, Mark, Zhao, Jifu, and Huff, Kathryn. A comparison of machine learning methods for automated gamma-ray spectroscopy.  United States: N. p., 2018. 
Web.  doi:10.1016/j.nima.2018.10.063.

Copy to clipboard


                    Kamuda, Mark, Zhao, Jifu, & Huff, Kathryn. A comparison of machine learning methods for automated gamma-ray spectroscopy.  United States.  https://doi.org/10.1016/j.nima.2018.10.063

Copy to clipboard


                    Kamuda, Mark, Zhao, Jifu, and Huff, Kathryn. Mon .  
"A comparison of machine learning methods for automated gamma-ray spectroscopy".  United States.  https://doi.org/10.1016/j.nima.2018.10.063.  https://www.osti.gov/servlets/purl/1524430.

Copy to clipboard


                    
@article{osti_1524430,

  title        = {A comparison of machine learning methods for automated gamma-ray spectroscopy},

  author       = {Kamuda, Mark and Zhao, Jifu and Huff, Kathryn},

  abstractNote = {Pattern recognition algorithms such as artificial neural networks (NNs) and convolution neural networks (CNNs) are prime candidates to perform automated gamma-ray spectroscopy. The way these models train and operate mimic how trained spectroscopists identify spectra. These models have shown promise in identifying gamma-ray spectra with large calibration drift and unknown background radiation fields. In this work, two algorithms for mixtures of radioisotopes based on NN and CNN are presented and evaluated.},

  doi          = {10.1016/j.nima.2018.10.063},

  journal      = {Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment},

  number       = ,

  volume       = 954,

  place        = {United States},

  year         = {Mon Oct 01 00:00:00 EDT 2018},

  month        = {Mon Oct 01 00:00:00 EDT 2018}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Accepted Manuscript (Publisher)

Accepted Manuscript (DOE)

Publisher's Version of Record

https://doi.org/10.1016/j.nima.2018.10.063

Other availability

Search WorldCat to find libraries that may hold this journal

Citation Metrics:

Cited by: 25 works

Citation information provided by
Web of Science

Save / Share:

Export Metadata

Save to My Library

Similar Records in DOE PAGES and OSTI.GOV collections:

Electron energy loss spectroscopy database synthesis and automation of core-loss edge recognition by deep-learning neural networks

Journal Article Kong, Lingli ; Ji, Zhengran ; Xin, Huolin L. - Scientific Reports

Abstract The ionization edges encoded in the electron energy loss spectroscopy (EELS) spectra enable advanced material analysis including composition analyses and elemental quantifications. The development of the parallel EELS instrument and fast, sensitive detectors have greatly improved the acquisition speed of EELS spectra. However, the traditional way of core-loss edge recognition is experience based and human labor dependent, which limits the processing speed. So far, the low signal–noise ratio and the low jump ratio of the core-loss edges on the raw EELS spectra have been challenging for the automation of edge recognition. In this work, a convolutional-bidirectional long short-term memorymore »« less
https://doi.org/10.1038/s41598-022-25870-3
Automated isotope identification algorithm using artificial neural networks

Journal Article Kamuda, Mark ; Stinnett, Jacob ; Sullivan, Clair - IEEE Transactions on Nuclear Science

There is a need to develop an algorithm that can determine the relative activities of radio-isotopes in a large dataset of low-resolution gamma-ray spectra that contains a mixture of many radio-isotopes. Low-resolution gamma-ray spectra that contain mixtures of radio-isotopes often exhibit feature over-lap, requiring algorithms that can analyze these features when overlap occurs. While machine learning and pattern recognition algorithms have shown promise for the problem of radio-isotope identification, their ability to identify and quantify mixtures of radio-isotopes has not been studied. Because machine learning algorithms use abstract features of the spectrum, such as the shape of overlapping peaks andmore »« less
Cited by 53
https://doi.org/10.1109/TNS.2017.2693152

Full Text Available
An automated isotope identification and quantification algorithm for isotope mixtures in low-resolution gamma-ray spectra

Journal Article Kamuda, Mark ; Sullivan, Clair J. - Radiation Physics and Chemistry (1993)

There is a need to develop an automated isotope identification and quantification algorithm that can perform well using low-resolution gamma-ray detectors. The algorithm should be able to perform well on spectra that contain a mixture of many isotopes as well as in cases where spectral features are difficult to analyze. Due to the low resolution of these detectors, spectra of isotope mixtures becomes complicated to identify when features overlap. Previous research applying machine learning algorithms to isotope identification has been promising. Further work is needed to demonstrate the limits of machine learning algorithms when applied to identifying mixtures of isotopesmore »« less
Cited by 29
https://doi.org/10.1016/j.radphyschem.2018.06.017

Full Text Available
Transfer Learning in Automated Gamma Spectral Identification

Conference Moore, Eric

The models and weights of prior trained Convolutional Neural Networks (CNN) created to perform automated isotopic classification of time-sequenced gamma-ray spectra, were utilized to provide source domain knowledge as training on new domains of potential interest. The previous results were achieved solely using modeled spectral data. In this work we attempt to transfer the knowledge gained to the new, if similar, domain of solely measured data. The ability to train on modeled data and predict on measured data will be crucial in any successful data-driven approach to this problem space.
Full Text Available
Hybrid geological modeling: Combining machine learning and multiple-point statistics

Journal Article Bai, Tao ; Tahmasebi, Pejman - Computers and Geosciences

Accurately modeling and constructing a geologically realistic subsurface model remains an outstanding problem as the morphology controls the flow behaviors. Particularly, one of the pattern-based methods, namely cross-correlation based simulation, has been proved to be an effective way to reconstruct a realistic model, at both small and large scales. However, conditioning to point data in the large-scale problems is still a crucial issue in these algorithms, since there is always a trade-off between the quality of the realizations and the degree of point data reproduction. Specifically, it is not practical to build a training image (TI) which includes all themore »« less
https://doi.org/10.1016/j.cageo.2020.104519

Full Text Available

Similar Records