Unfolding the fast neutron spectra of a BC501A liquid scintillation detector using GRAVEL method

Chen, Yonghao; Chen, Ximeng; Jiarong, Lei; Liu, An; Zhang, Xiaodong; Shao, Jianxiong; Peng, Zheng; Wang, Xinhua

doi:10.1007/s11433-014-5553-7

Cited by 40 publications

(27 citation statements)

References 17 publications

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“…8 , in which the difference between the neutron energy spectra (unfolded and ISO) is presented. As shown, the size of the errors for calculations performed using ANFIS is much smaller than the errors reported in the previously published works [ 14 , 19 ]. As displayed in Figs 6 – 8 , the unfolded energy spectra of a neutron sources obtained using ANFIS are more accurate than the results obtained in the previously published paper by Hosseini [ 10 ].…”

Section: Resultscontrasting

confidence: 54%

Evaluation of a new neutron energy spectrum unfolding code based on an Adaptive Neuro-Fuzzy Inference System (ANFIS)

Hosseini

Afrakoti

2017

Journal of Radiation Research

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The purpose of the present study was to reconstruct the energy spectrum of a poly-energetic neutron source using an algorithm developed based on an Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS is a kind of artificial neural network based on the Takagi–Sugeno fuzzy inference system. The ANFIS algorithm uses the advantages of both fuzzy inference systems and artificial neural networks to improve the effectiveness of algorithms in various applications such as modeling, control and classification. The neutron pulse height distributions used as input data in the training procedure for the ANFIS algorithm were obtained from the simulations performed by MCNPX-ESUT computational code (MCNPX-Energy engineering of Sharif University of Technology). Taking into account the normalization condition of each energy spectrum, 4300 neutron energy spectra were generated randomly. (The value in each bin was generated randomly, and finally a normalization of each generated energy spectrum was performed). The randomly generated neutron energy spectra were considered as output data of the developed ANFIS computational code in the training step. To calculate the neutron energy spectrum using conventional methods, an inverse problem with an approximately singular response matrix (with the determinant of the matrix close to zero) should be solved. The solution of the inverse problem using the conventional methods unfold neutron energy spectrum with low accuracy. Application of the iterative algorithms in the solution of such a problem, or utilizing the intelligent algorithms (in which there is no need to solve the problem), is usually preferred for unfolding of the energy spectrum. Therefore, the main reason for development of intelligent algorithms like ANFIS for unfolding of neutron energy spectra is to avoid solving the inverse problem. In the present study, the unfolded neutron energy spectra of 252Cf and 241Am-9Be neutron sources using the developed computational code were found to have excellent agreement with the reference data. Also, the unfolded energy spectra of the neutron sources as obtained using ANFIS were more accurate than the results reported from calculations performed using artificial neural networks in previously published papers.

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Section: Resultscontrasting

confidence: 54%

Evaluation of a new neutron energy spectrum unfolding code based on an Adaptive Neuro-Fuzzy Inference System (ANFIS)

Hosseini

Afrakoti

2017

Journal of Radiation Research

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“…We assess the performance of proposed algorithm (referred to as MCMC in the remainder of the paper) with GRAVEL [9], [41], [47], Tik (Tikhonov regularization with L-curve method) [39] and PIDAL [36] applied to simulated neutron sources. We consider three sources: 2.5 MeV monoenergetic neutron source, 252 Cf and 241 AmBe.…”

Section: Unfolding Results and Discussionmentioning

confidence: 99%

A Hierarchical Bayesian Approach to Neutron Spectrum Unfolding With Organic Scintillators

Zhu

Altmann

Fulvio

et al. 2019

IEEE Trans. Nucl. Sci.

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We propose a hierarchical Bayesian model and stateof-art Monte Carlo sampling method to solve the unfolding problem, i.e., to estimate the spectrum of an unknown neutron source from the data detected by an organic scintillator. Inferring neutron spectra is important for several applications, including nonproliferation and nuclear security, as it allows the discrimination of fission sources in special nuclear material (SNM) from other types of neutron sources based on the differences of the emitted neutron spectra. Organic scintillators interact with neutrons mostly via elastic scattering on hydrogen nuclei and therefore partially retain neutron energy information. Consequently, the neutron spectrum can be derived through deconvolution of the measured light output spectrum and the response functions of the scintillator to monoenergetic neutrons. The proposed approach is compared to three existing methods using simulated data to enable controlled benchmarks. We consider three sets of detector responses. One set corresponds to a 2.5 MeV monoenergetic neutron source and two sets are associated with (energy-wise) continuous neutron sources ( 252 Cf and 241 AmBe). Our results show that the proposed method has similar or better unfolding performance compared to other iterative or Tikhonov regularization-based approaches in terms of accuracy and robustness against limited detection events, while requiring less user supervision. The proposed method also provides a posteriori confidence measures, which offers additional information regarding the uncertainty of the measurements and the extracted information.

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“…Equations 1 through 3 are ill-conditioned equations system with an infinite number of solutions which have motivated researches to propose new and complementary approaches [38][39][40][41][42].…”

Section: Neutron Spectrometry With Artificial Neural Networkmentioning

confidence: 99%

Synapse. A Neutron Spectrum Unfolding Code Based on Generalized Regression Artificial Neural Networks

Martínez-Blanco¹,

Serrano-Muñoz²,

Vega-Carrillo³

et al. 2020

EAI Endorsed Transactions on Industrial Networks and Intelligen

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In its broadest sense, the term artificial intelligence indicates the ability of an artifact to perform the same types of functions that characterize human thought. The goal of AI is to use algorithms, heuristics and methodologies based on the ways in which the human brain solves problems. Artificial neural networks recreate the structure of the human brain imitating the learning process. The Artificial neural networks theory has provided an alternative to classical computing for those problems in which traditional methods have delivered results that are not very convincing or not very convenient such as in the case of the neutron spectrometry and dosimetry problem for radiation protection purposes, using the Bonner spheres spectrometer as measurement system, mainly because many problems are encountered when trying to determine the neutron energy spectrum of a measured data. The most delicate part of the spectrometry based on this system is the unfolding process, for which several neutron spectrum unfolding codes have being developed. However, these codes require an initial guess spectrum in order to initiate the unfolding process. Their poor availability and their not easy management for the end user are other associated problems. Artificial Intelligence technology, is an alternative technique that is gaining popularity among researchers in neutron spectrometry research area, since it offers better results compared with the traditional solution methods. In this work, "Synapse", a neutron spectrum unfolding code based on Generalized Regression Artificial Neural Networks technology is presented. The Synapse code is capable to unfold the neutron spectrum and to calculate 15 dosimetric quantities using the count rates, coming from a BSS as the only entrance information. The results obtained show that the Synapse code, based on GRANN technology, is a promising and innovative technological alternative for solving the neutron spectrometry and dosimetry problems.

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Unfolding the fast neutron spectra of a BC501A liquid scintillation detector using GRAVEL method

Cited by 40 publications

References 17 publications

Evaluation of a new neutron energy spectrum unfolding code based on an Adaptive Neuro-Fuzzy Inference System (ANFIS)

Evaluation of a new neutron energy spectrum unfolding code based on an Adaptive Neuro-Fuzzy Inference System (ANFIS)

A Hierarchical Bayesian Approach to Neutron Spectrum Unfolding With Organic Scintillators

Synapse. A Neutron Spectrum Unfolding Code Based on Generalized Regression Artificial Neural Networks

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