Detector response unfolding using artificial neural networks

Avdic, Senada; Pozzi, Sara A.; Protopopescu, V.

doi:10.1016/j.nima.2006.06.023

Cited by 40 publications

(22 citation statements)

References 8 publications

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“…The accurate unfolding of neutron spectra increases the sensitivity of assays performed on various nuclear materials [ 2 ]. The use of interrogation and reconstruction techniques for detection of special nuclear materials and neutron sources via energy spectra has been widely investigated [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

“…Modern unfolding code packages that employ these techniques include MAXED [ 6 ], UNFANA [ 7 ], HEPRO [ 8 ] and FORIST [ 9 ]. Other methods, such as using an artificial neural network [ 3 , 10 ], may be used for the unfolding of neutron energy spectra. The unfolded neutron energy spectrum obtained using an artificial neural network has acceptable accuracy [ 10 ]; however, the calculation cost reported in the cited papers is relatively high.…”

Section: Introductionmentioning

confidence: 99%

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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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…Other articles on the response of scintillator materials to neutrons and gamma rays have been published, including Refs. [12][13][14][15][16][17][18][19][20][21][22][23][24]. Simulations of spectra resulting from photon interactions for a variety of sources with NaI(Tl) and PVT-based gamma-ray detectors have been reported by Siciliano et al [25].…”

Section: Introductionmentioning

confidence: 99%

Energy calibration of gamma spectra in plastic scintillators using Compton kinematics

Siciliano

Ely

Kouzes

et al. 2008

Nuclear Instruments and Methods in Physics Research Section A:

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“…In order to carry out this process, several mathematical methods and computing algorithms, such as the least-squares [1], iterative [2], Monte Carlo methods [3], and genetic algorithm [4] have been proposed. In addition to these traditional methods, the artificial neural networks (ANN) [5][6][7] are now being used in a wide variety of data processing applications. The ANN technique is suitable for unfolding the neutron spectra and has such advantages as saving time and requiring few samples.…”

Section: Introductionmentioning

confidence: 99%

“…The training data are a set of response functions of monoenergetic neutrons generated by the Monte Carlo code O5S [8] according to the actual BC501A scintillation detector with the same size, and the training method is the resilient backpropagation algorithm recommended by Senada [6]. An energy spectra measurement system was also set up for the validation of the unfolding capability by using Am-Be neutron source and D-T neutron source.…”

Section: Introductionmentioning

confidence: 99%

Application of artificial neural networks for unfolding neutron spectra by using a scintillation detector

Yan

Liu

et al. 2011

Sci. China Phys. Mech. Astron.

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The unfolding of neutron spectra from the pulse height distribution measured by a BC501A scintillation detector is accomplished by the application of artificial neural networks (ANN). A simple linear neural network without biases and hidden layers is adopted. A set of monoenergetic detector response functions in the energy range from 0.25 MeV to 16 MeV with an energy interval of 0.25 MeV are generated by the Monte Carlo code O5S in the training phase of the unfolding process. The capability of ANN was demonstrated successfully using the Monte Carlo data itself and experimental data obtained from the Am-Be neutron source and D-T neutron source. artificial neural network, unfolding neutron spectra, scintillation detectors, O5S

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Detector response unfolding using artificial neural networks

Cited by 40 publications

References 8 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)

Energy calibration of gamma spectra in plastic scintillators using Compton kinematics

Application of artificial neural networks for unfolding neutron spectra by using a scintillation detector

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