Gamma peak search and peak fitting algorithm for a low-resolution detector with applications in gamma spectroscopy

Brillouin frequency shift (BFS) of distributed optical fiber sensor is extracted from the Brillouin gain spectrum (BGS), which is often characterized by Lorenz type. However, in the case of complex stress and optical fiber self damage, the BGS will deviate from Lorenz type and be asymmetric, which leads to the extraction error of BFS. In order to enhance the extraction accuracy of BFS, the Lorenz local single peak fitting algorithm was developed to fit the Brillouin gain spectrum curve, which can make the BSG symmetrical with respect to the Brillouin center frequency shift. One temperature test of a fiber-reinforced polymer (FRP) packaged sensor whose BSG curve is asymmetric was conducted to verify the idea. The results show that the local region curve of BSG processed by the developed algorithm has good symmetry, and the temperature measurement accuracy obtained by the developed algorithm is higher than that directly measured by demodulation equipment. Comparison with the reference temperature, the relative measurement error measured by the developed algorithm and BOTDA are within 4% and 8%, respectively.

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Potential radionuclide emissions from stacks on the Hanford site, Part 2: Dose assessment methodology using portable low-resolution gamma spectroscopy

10.2172/95656 ◽

1995 ◽

Author(s):

J M Barnett

Keyword(s):

Gamma Spectroscopy ◽

Dose Assessment ◽

Assessment Methodology ◽

Low Resolution ◽

Hanford Site

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3P003 Segmentation & fitting algorithm for multiple subunit fitting into a low resolution density map(01A. Protein: Structure,Poster,The 52nd Annual Meeting of the Biophysical Society of Japan(BSJ2014))

Seibutsu Butsuri ◽

10.2142/biophys.54.s249_3 ◽

2014 ◽

Vol 54 (supplement1-2) ◽

pp. S249

Author(s):

Takeshi Kawabata ◽

Hirofumi Suzuki ◽

Haruki Nakamura

Keyword(s):

Protein Structure ◽

Annual Meeting ◽

Low Resolution ◽

Fitting Algorithm ◽

Biophysical Society ◽

Density Map

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Benefits of Low Resolution Gamma Spectroscopy (LRGS) in Assessment and Remediation of Alpha Contamination

ASME 2009 12th International Conference on Environmental Remediation and Radioactive Waste Management, Volume 2 ◽

10.1115/icem2009-16023 ◽

2009 ◽

Author(s):

Martha McBarron ◽

Jim Cassidy ◽

Louise Hutton

Keyword(s):

Gamma Spectroscopy ◽

Cost Effective ◽

Low Resolution ◽

Site Assessment ◽

Field Instrumentation ◽

Cost Effective Alternative ◽

Storage Area ◽

Sampling Programme ◽

Real Time Identification

The legacy of alpha contamination in an exterior disused storage area at the Low Level Waste Repository (LLWR) presented difficult challenges with regard to assessment and remediation. The area was heavily overgrown, with degraded remnant surfaces and debris from past demolition activities. The use of conventional field instruments and/or an extensive intrusive sampling programme were precluded as being impractical, the latter prohibitively costly and not expeditious. This paper describes the identification of a cost-effective alternative for the initial site assessment and the role of LRGS in the subsequent remediation work. It includes: 1 identification and selection of a field instrumentation/system for investigating the site. 2 details of the TERRIER™ system, incorporating the SAM935 Low Resolution Gamma Spectrometer (LRGS) with the capability of real time identification of nuclides. 3 use of the TERRIER™ for a non-intrusive survey of the site, with rapid transformation of field data into informative plots useful for remediation planning. 4 use of the TERRIER™ during remediation work, including assay of arisings. 5 benefits and limitations of using LRGS to support remediation of alpha contamination. 6 application of the instrumentation/system to other radiological investigations.

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Atom Probe Tomography for Isotopic Analysis: Development of the 34S/32S System in Sulfides

Microscopy and Microanalysis ◽

10.1017/s1431927621013568 ◽

2021 ◽

pp. 1-14

Author(s):

Phillip Gopon ◽

James O. Douglas ◽

Frederick Meisenkothen ◽

Jaspreet Singh ◽

Andrew J. London ◽

...

Keyword(s):

Atom Probe Tomography ◽

Time Of Flight ◽

Simulated Data ◽

Isotopic Analysis ◽

Atom Probe ◽

Standard Material ◽

Peak Fitting ◽

Isotopic Analyses ◽

Fitting Algorithm ◽

Adaptive Peak

Using a combination of simulated data and pyrite isotopic reference materials, we have refined a methodology to obtain quantitative δ34S measurements from atom probe tomography (APT) datasets. This study builds on previous attempts to characterize relative 34S/32S ratios in gold-containing pyrite using APT. We have also improved our understanding of the artifacts inherent in laser-pulsed APT of insulators. Specifically, we find the probability of multi-hit detection events increases during the APT experiment, which can have a detrimental effect on the accuracy of the analysis. We demonstrate the use of standardized corrected time-of-flight single-hit data for our isotopic analysis. Additionally, we identify issues with the standard methods of extracting background-corrected counts from APT mass spectra. These lead to inaccurate and inconsistent isotopic analyses due to human variability in peak ranging and issues with background correction algorithms. In this study, we use the corrected time-of-flight single-hit data, an adaptive peak fitting algorithm, and an improved deconvolution algorithm to extract 34S/32S ratios from the S2+ peaks. By analyzing against a standard material, acquired under similar conditions, we have extracted δ34S values to within ±5‰ (1‰ = 1 part per thousand) of the published values of our standards.

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Brillouin Peak Fitting Algorithm Based on Correlation-LM Method

Laser & Optoelectronics Progress ◽

10.3788/lop56.010602 ◽

2019 ◽

Vol 56 (1) ◽

pp. 010602

Author(s):

高树国 Gao Shuguo ◽

刘云鹏 Liu Yunpeng ◽

李欢 Li Huan ◽

田源 Tian Yuan ◽

范晓舟 Fan Xiaozhou ◽

...

Keyword(s):

Peak Fitting ◽

Fitting Algorithm

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Evaluasi Implementasi Algoritma Machine Learning K-Nearest Neighbors (kNN) pada Data Spektroskopi Gamma Resolusi Rendah

Al-Fiziya: Journal of Materials Science, Geophysics, Instrumentation and Theoretical Physics ◽

10.15408/fiziya.v3i1.16180 ◽

2020 ◽

Vol 3 (1) ◽

pp. 9-14

Author(s):

Muhammad Sholih Fajri ◽

Nizar Septian ◽

Edy Sanjaya

Keyword(s):

Machine Learning ◽

Test Data ◽

Learning Algorithm ◽

Gamma Spectroscopy ◽

Nearest Neighbors ◽

Data Type ◽

Machine Learning Algorithm ◽

Spectroscopy Data ◽

Low Resolution ◽

K Nearest Neighbors

Abstrak Pada artikel ini kami mengevaluasi bagaimana implementasi algoritma machine learning k-Nearest Neighbors (kNN) pada data spektroskopi gamma beresolusi rendah. Penelitian ini bertujuan untuk mengetahui bagaimana performa kNN dalam mempelajari data tersebut. Kami melakukan berbagai variasi, yaitu: jumlah data training, jumlah data tes, jenis metric, dan nilai k untuk memperoleh performa terbaik dari algoritma ini. Data spektroskopi gamma diambil menggunakan sintilator NaI(Tl) Leybold Didactic dengan resolusi energi sebesar 10.9 keV per channel. Hasil variasi menunjukkan bahwa algoritma kNN memberikan hasil prediksi klasifikasi radioisotop yang sangat fluktuatif. Abstract In this paper we evaluate the implementation of a machine learning algorithm namely k-Nearest Neighbors (kNN) on low resolution gamma spectroscopy data. The aim is to provide the information of how well the algorithm performs on learning the data. We did the variation of number of training and test data, type of metric used, and values of k in order to see the best performance of the algorithm. The gamma spectroscopy data were taken using NaI(Tl) scintillator made by Leybold Didactic with resolution of 10.9 keV per channel. The variations show that the kNN algorithm produce significantly fluctuating accuracy to the prediction of radioisotope class.

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