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Processing scintillation gamma-ray spectra by artificial neural network

Shahabinejad, H ; Sharif University of Technology | 2020

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  1. Type of Document: Article
  2. DOI: 10.1007/s10967-020-07239-w
  3. Publisher: Springer Netherlands , 2020
  4. Abstract:
  5. Elemental analysis can be performed using obtained gamma-ray spectrum of the sample under study. In this work, simple Multi-Layer Perceptron (MLP) neural network models are proposed for analyzing a gamma-ray emitting sample using whole information of its obtained gamma-ray spectrum. Elemental analysis is performed in two fields of study using 3 × 3 inch NaI(Tl) detectors: Radio-Isotope Identification (RIID) and Prompt Gamma Neutron Activation Analysis (PGNAA). The gamma-ray point sources are used for an empirical study in RIID field, while a Monte Carlo simulation study is considered for determining chlorine and water content of crude oil using combination of PGNAA technique and a MLP model. According to the obtained results of both empirical and simulation studies, the proposed ANN models are appropriate for elemental analysis using whole gamma-ray spectral information of sample under study. © 2020, Akadémiai Kiadó, Budapest, Hungary
  6. Keywords:
  7. Artificial neural network ; Prompt gamma neutron activation analysis ; Radioisotope identification ; Americium 241 ; Cesium 137 ; Chlorine ; Cobalt 60 ; Europium 152 ; Magnesium oxide ; Petroleum ; Sodium 22 ; Sodium iodide ; Water ; Empiricism ; Gamma radiation ; Gamma spectrometry ; Isotope analysis ; Monte Carlo method ; Multilayer perceptron ; Scintillation ; Elemental analysis
  8. Source: Journal of Radioanalytical and Nuclear Chemistry ; Volume 325, Issue 2 , 2020 , Pages 471-483
  9. URL: https://link.springer.com/article/10.1007/s10967-020-07239-w