Document Type : Technical Paper
Authors
1 Department of Physics and Energy Engineering, Amirkabir University of Technology, P. Box: 1591634311, Tehran, Iran
2 Department of Electrical and Computer Engineering, Sungkyunkwan University, P-Box: 16419, Suwon, Republic of Korea
Abstract
This paper proposes a novel approach for generating high-resolution energy spectra using cost-effective Sodium iodide Thallium activated (NaI(Tl)) detectors. It employ a multi-output regression chain structure based on support vector regression (SVR) to map NaI(Tl) spectra to their corresponding HPGe spectra. The suggested framework utilizes a regression chain strategy to enhance regression models that lack support for multi-output regression. This involves initially employing one regressor for each energy channel of the HPGe spectrum. Subsequently, multiple regressors are integrated to forecast all energy channels of HPGe spectrum. Each regressor in the chain receives the entire NaI spectrum as input. Then, for each subsequent regressor, input is further augmented by concatenating the outputs of all preceding regressors in the chain. Despite being trained on a limited radioisotope library, the model exhibits exceptional performance across diverse measured test spectra containing multiple radioisotopes. Among the various kernel functions employed (linear, radial basis function (RBF), and polynomial), the RBF and polynomial kernels yielded superior performance compared to the linear kernel. By enabling HPGe spectrum prediction using NaI(Tl) detectors, this study highlights a significant advancement in radiation detection capabilities, addressing cost and operational considerations.
Keywords
- Multi-output regression
- NaI detector
- HPGe detector
- Gamma spectroscopy
- Support vector regression
- Radioisotope
Main Subjects
- Gilmore G. Practical gamma-ray spectroscopy. John Wiley & Sons; 2008 May 27.
- Cresswell AJ, Sanderson DC, Yamaguchi K. Assessment of the calibration of gamma spectrometry systems in forest environments. Journal of Environmental Radioactivity. 2018 Jan 1;181:70-7.
- Maacha L, Jaffal M, Jarni A, Kchikach A, Mouguina EM, Zouhair M, Ennaciri A, Saddiqi O. A contribution of airborne magnetic, gamma ray spectrometric data in understanding the structure of the Central Jebilet Hercynian massif and implications for mining. Journal of African Earth Sciences. 2017 Oct 1;134:389-403.
- Söderström M, Eriksson J. Gamma-ray spectrometry and geological maps as tools for cadmium risk assessment in arable soils. Geoderma. 2013 Jan 1;192:323-34. Khan HM, Chaudhry ZS, Ismail M, Khan K. Assessment of Radionuclides, Trace Metals and Radionuclide Transfer from Soil to Food of Jhangar Valley (Pakistan) Using Gamma-Ray Spectrometry. Water, Air, & Soil Pollution. 2010;213(1–4):353–62.
- Khan HM, Chaudhry ZS, Ismail M, Khan K. Assessment of radionuclides, trace metals and radionuclide transfer from soil to food of Jhangar Valley (Pakistan) using gamma-ray spectrometry. Water, Air, & Soil Pollution. 2010 Nov;213:353-62.
- Hung NQ, Chuong HD, Thanh TT, Van Tao C. Intercomparison NaI (Tl) and HPGe spectrometry to studies of natural radioactivity on geological samples. Journal of environmental radioactivity. 2016 Nov 1;164:197-201.
- Knoll GF. Radiation detection and measurement. John Wiley & Sons; 2010 Aug 16.
- Debertin K, Helmer RG. Gamma-and X-ray spectrometry with semiconductor detectors. iaea.org, 1988.
- Kong H, Ye F, Lu X, Guo L, Tian J, Xu G. Deconvolution of overlapped peaks based on the exponentially modified Gaussian model in comprehensive two-dimensional gas chromatography. Journal of Chromatography A. 2005 Sep 9;1086 (1-2):160-4.
- Hu Y, Li W, Hu J. Resolving overlapped spectra with curve fitting. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. 2005 Nov 1;62(1-3):16-21.
- Liu M, Dong Z, Xin G, Sun Y, Qu R. An improved method based on a new wavelet transform for overlapped peak detection on spectrum obtained by portable Raman system. Chemometrics and intelligent laboratory systems. 2018 Nov 15;182:1-8.
- Saeidi Z, Afarideh H, Ghergherehchi M. Utilizing artificial neural networks to convert gamma-ray spectra from NaI (Tl) detectors to HPGe detector gamma-ray spectra. Annals of Nuclear Energy. 2024 Jun 1;200:110368.
- Moshkbar-Bakhshayesh K. Constructing energy spectrum of inorganic scintillator based on plastic scintillator by different kernel functions of SVM learning algorithm and TSC data mapping. Journal of Instrumentation. 2020 Jan 24;15(01):P01028.
- Drucker H, Burges CJ, Kaufman L, Smola A, Vapnik V. Support vector regression machines. Advances in neural information processing systems. 1996;9.
- Borchani H, Varando G, Bielza C, Larranaga P. A survey on multi‐output regression. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2015 Sep;5(5):216-33.
- Mao T, Lee S, Ou Y, Mihaita AS. Transport multi-mode choice prediction using a hybrid multi-output regression modelling. net
- Sánchez-Fernández M, de-Prado-Cumplido M, Arenas-García J, Pérez-Cruz F. SVM multiregression for nonlinear channel estimation in multiple-input multiple-output systems. IEEE transactions on signal processing. 2004 Jul 19;52(8):2298-307.
- Vapnik VN. Pattern recognition using generalized portrait method. Automation and remote control. 1963;24(6):774-80.
- Boser BE, Guyon IM, Vapnik VN. A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop on Computational learning theory 1992 Jul 1 (pp. 144-152).
- Cheng K, Lu Z, Wei Y, Shi Y, Zhou Y. Mixed kernel function support vector regression for global sensitivity analysis. Mechanical Systems and Signal Processing. 2017 Nov 1;96:201-14.
)