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

Accurate knowledge of the neutron energy spectra is useful in basic research and applications. The overall procedure of measuring and unfolding the fast neutron energy spectra with BC501A liquid scintillation detector is described. The recoil proton spectrum of 241Am-Be neutrons was obtained experimentally. With the NRESP7 code, the response matrix of detector was simulated. Combining the recoil proton spectrum and response matrix, the unfolding of neutron spectra was performed by GRAVEL iterative algorithm. A MatLab program based on the GRAVEL method was developed. The continuous neutron spectrum of ²⁴¹Am-Be source and monoenergetic neutron spectrum of D-T source have been unfolded successfully and are in good agreement with their standard reference spectra. The unfolded ²⁴¹Am-Be spectrum are more accurate than the spectra unfolded by artificial neural networks in recent years.     

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

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 demon...     

人工神经网络法鉴别红外光谱

本文将反向传播人工神经网络（ＢＰ－ＡＮＮ）用于ＦＴＩＲ,鉴别未知化合物。结果表明,当训练集样本不含噪声时,纯光谱的预测结果很好。而当训练集样本有少量噪声干扰时,预测结果随预测集样本的不同,而得到不同的改善。     

The criteria for selecting a method for unfolding neutron spectra based on the information entropy theory

To further expand the application of an artificial neural network in the field of neutron spectrometry, the criteria for choosing between an artificial neural network and the maximum entropy method for the purpose of unfolding neutron spectra was presented. The counts of the Bonner spheres for IAEA neutron spectra were used as a database, and the artificial neural network and the maximum entropy method were used to unfold neutron spectra; the mean squares of the spectra were defined as the differences between the desired and unfolded spectra. After the information entropy of each spectrum was calculated using information entropy theory, the relationship between the mean squares of the spectra and the information entropy was acquired. Useful information from the information entropy guided the selection of unfolding methods. Due to the importance of the information entropy, the method for predicting the information entropy using the Bonner spheres' counts was established. The criteria based on the information entropy theory can be used to choose between the artificial neural network and the maximum entropy method unfolding methods. The application of an artificial neural network to unfold neutron spectra was expanded.     

D-T中子与平板型聚乙烯材料作用的次级中子角度谱实验研究

基于反冲质子法建立了一种测量D-T中子与平板型宏观样品作用的次级中子角度谱的实验方法.为保证探测器的能量线性并在较低的中子有效测量下阈(0.5 MeV)情况下获得好的中子-伽马射线甄别性能,采用高、低能段分别测量的方法.采用事件记录法,同时记录了次级中子和伴随伽马射线的脉冲形状甄别和脉冲幅度二维信息,利用基于ROOT数据分析平台编写的离线数据分析程序,完成了伴随伽马射线的挑选和扣除,以及高、低两能段反冲质子谱的拼接,并成功的将神经网络技术应用于中子能谱的解谱,获得了D-T中子与9和18 cm厚平板型聚乙烯材料作用的0.5-15 MeV的次级中子角度谱实验结果.实验模型的MC模拟由MCNP5完成,数据库采用ENDF-VI,实验结果和MC计算结果在实验不确定度范围内一致. 关键词： D-T中子 积分中子学 反冲质子法 次级中子能谱     

Potential reduction interior point algorithm unfolding of neutron energy spectrum measured by recoil proton method

A reconstruction algorithm for unfolding neutron energy spectra has been developed, based for the first time on the potential reduction interior point algorithm. This algorithm can be easily applied to neutron energy spectrum reconstruction in the recoil proton method. We transform the neutron energy spectrum unfolding problem into a typical nonnegative linear complementarity problem. The recoil proton energy spectrum and response matrix at angles of 0^o and 30^o are generated by the Geant4 simulation toolkit. Several different neutron energy test spectra are also employed. It is found that this unfolding algorithm is stable and provides efficient, accurate results.     

Neutron fluence spectrometry using disk activation

A simple and robust detector for spectrometry of environmental neutrons has been developed. The technique is based on neutron activation of a series of different metal disks followed by low-level gamma-ray spectrometry of the activated disks and subsequent neutron spectrum unfolding. The technique is similar to foil activation but here the applied neutron fluence rates are much lower than usually in the case of foil activation. The detector has been tested in quasi mono-energetic neutron fields with fluence rates in the order of 1000–10000 cm⁻² s⁻¹, where the obtained spectra showed good agreement with spectra measured using a Bonner sphere spectrometer. The detector has also been tested using an AmBe source and at a neutron fluence rate of about 40 cm⁻² s⁻¹, again, a good agreement with the assumed spectrum was achieved.     

The effects of shielding around the source on neutron energy spectra

The knowledge of neutron energy spectra contributes to unambiguous identification of neutron sources in the fields of nuclear safeguards and nuclear non-proliferation. Since a real scenario situation includes the presence of shielding around the source, we have investigated the influence of the potential shielding surrounding the source on the shape of energy spectra for a few neutron sources. We have applied the maximum-likelihood, expectation–maximisation (MLEM) method with one-step-late (OSL) algorithm for neutron spectra unfolding. The pulse height distributions used in the unfolding procedures were simulated with the high accuracy by using the MCNP-PoliMi code based on the Monte Carlo method. A possibility to identify the shielded neutron sources by using the unfolding method was examined with two continuous-in-energy sources, such as ²⁵²Cf and ²⁴¹Am–Be in source-shielding configurations with lead (Pb) and polyethylene (PE) blocks. The results of calculations have shown that the identification of ²⁵²Cf and ²⁴¹Am–Be sources with 2.5 cm of Pb and PE shield can be achieved successfully by using the MLEM method with the OSL algorithm. However, the unfolded results for ²⁵²Cf and ²⁴¹Am–Be sources with 10 cm of PE shield significantly deviate from the reference spectra and the sources cannot be correctly identified on the basis of their unfolded energy spectra.     

Be(d,n)反应中子源能谱测量及应用

 利用中子飞行时间技术和BC501A液体闪烁探测器的粒子分辨特性,测量了0°方向、20 MeV氘束轰击厚金属铍靶反应产生的中子源能谱,测量的中子能谱范围为0.7～25.0 MeV。在60°方向放置芪晶体闪烁探测器,由刻度好的BC501A液体闪烁探测器归一校正后,用于中子源强度监测。利用Be(d, n) 反应中子源,采用单粒子灵敏度标定方法,实验标定了0.75~15.75 MeV能量范围内的薄膜闪烁探测器中子能量响应曲线,实验结果与蒙特卡罗模拟计算结果在8%的不确定度范围内一致。     

The application of artificial neural networks to the inversion of the positron lifetime spectrum

A new method of processing positron annihilation lifetime spectra is proposed.It is based on an artificial neural network(ANN)-back propagation network(BPN).By using data from simulated positron lifetime spectra which are generated by a simulation program and tested by other analysis programs,the BPN can be trained to extract lifetime and intensity from a positron annihilation lifetime spectrum as an input.In principle,the method has the potential to unfold an unknown number of lifetimes and their intensities from a measured spectrum.So far,only a proof-of-principle type preliminary investigation was made by unfolding three or four discrete lifetimes.The present study aims to design the network.Besides,the performance of this method requires both the accurate design of the BPN structure and a long training time.In addition,the performance of the method in practical applications is dependent on the quality of the simulation model.However,the chances of satisfying the above criteria appear to be high.When appropriately developed,a trained network could be a very efficient alternative to the existing methods,with a very short identification time.We have used the artificial neural network codes to analyze data such as the positron lifetime spectra for single crystal materials and monocrystalline silicon.Some meaningful results are obtained.     

Hyperparameter on-line learning of stochastic resonance based threshold networks

Aiming at training the feed-forward threshold neural network consisting of nondifferentiable activation functions, the approach of noise injection forms a stochastic resonance based threshold network that can be optimized by various gradient-based optimizers. The introduction of injected noise extends the noise level into the parameter space of the designed threshold network, but leads to a highly non-convex optimization landscape of the loss function. Thus, the hyperparameter on-line learning procedure with respective to network weights and noise levels becomes of challenge. It is shown that the Adam optimizer, as an adaptive variant of stochastic gradient descent, manifests its superior learning ability in training the stochastic resonance based threshold network effectively. Experimental results demonstrate the significant improvement of performance of the designed threshold network trained by the Adam optimizer for function approximation and image classification.     

Response of a Si-diode-based device to fast neutrons

Semiconductor devices based on a Si-detector are frequently used for charged particle's detection; one application being in the investigation of cosmic radiation fields. From the spectra of energy deposition events in such devices, the total energy deposited by the radiation in silicon can be derived. This contribution presents the results of studies concerning the response of this type of detector to fast neutrons. First, the spectrum of energy deposition was established in fast neutron radiation fields with average energies from 0.5 to 50 MeV. It was found that these spectra vary significantly with the neutron energy. The comparison with the spectra registered in photon beams permitted an estimation of the part of energy deposited that could be attributed to neutrons. It was found that this part increases rapidly with neutron energy. The possibilities to use this type of detector for neutron detection and dosimetry for radiation protection are analysed and discussed.     

LS301快中子探测器的效率刻度

主要介绍了利用252Cf标准裂变中子能谱刻度快脉冲LS301型液闪中子探测器的探测效率的实验方法和结果, 简要介绍了实验数据的处理过程, 得到了阈值分别为0.5, 0.7, 1.0和1.6 MeV, 以及中子能量在10 MeV以下的探测器效率, 并对测量结果进行了误差分析。同时为了检验实验结果的准确性, 将实验结果与理论计算结果进行比较, 两者在不确定度范围内符合得很好。Neutron detection efficiency of LS301 fast neutron detector was calibrated by measuring the neutron energy spectrum of 252Cf source, which has a standard fission neutron spectrum. A low mass, fast ionization chamber is used as the fission fragments detector in the time of flight(TOF) spectrometer and afforded the start signal of neutron flight. The stop signal was offered by the anode of LS301. A measured TOF spectrum was turned to the neutron energy spectrum which will be compared with the standard one. Consequently, the fast neutron detection efficiency of LS301 was obtained. Calibration procedures of experimental and data processing was given. Relative detection efficiencies were obtained separately for threshold settings of 0.5, 0.7, 1.0 and 1.6 MeV for neutron energies under 10 MeV. Experimental results accorded with theoretical efficiency curves which were calculated with the Monte Carlo code NEFF50.     

Feedforward Neural Network Trained by BFGS Algorithm for Modeling Plasma Etching of Silicon Carbide

Electron cyclotron resonance (ECR) plasma etching of silicon carbide is numerically modeled by a feedforward neural network (FNN), which is trained by the Broyden, Fletcher, Goldfarb, and Shanno (BFGS) optimization algorithm and the conventional backpropagation (BP) algorithm. The training samples are obtained from our experimental results, which meet the requirement of Box–Wilson central-composite-designed experimental test design. By using the samples, the BFGS algorithm is compared with the conventional BP algorithm with different hidden neuron numbers, different number of iterations and various learning rates. It is shown that the BFGS algorithm requires less hidden neurons and less iteration to obtain the same training results, and it also provides much smaller cross-validation errors. Therefore, the FNN trained by the BFGS algorithm possesses much better approximation and generalization ability. The silicon carbide ECR process modeling results demonstrate that the FNN trained by the BFGS algorithm are fast, reliable, and accurate.      

非线性荧光光谱的神经网络分析及其应用

大功率超快脉冲激光和气体相互作用可产生非线性荧光光谱,不同的气体分子具有不同的非线性荧光光谱。因而这种光谱可以作为物质的指纹模式加以识别分类,进而获知气体的成分。由于不同气体分子的光谱在同一波段上有很大的交叉重叠,用传统的光谱分析方法分析存在困难,采用神经网络方法分析上述非线性荧光光谱,利用经过预处理的荧光光谱数据作为模式样本,其中一部分样本作为学习样本对级联神经网络进行训练,用训练好的网络对所有样本进行实时识别,学习样本和测试样本的的正确识别率均可达100％,结果表明此方法可实时判断混合气体的组分。     

A telescope detection system for direct and high resolution spectrometry of intense neutron fields

A high energy- and spatial-resolution telescope detector was designed and constructed for neutron spectrometry of intense neutron fields. The detector is constituted by a plastic scintillator coupled to a monolithic silicon telescope (MST), in turn consisting of a ΔE and an E stage. The scintillator behaves as an “active” recoil-proton converter, since it measures the deposited energy of the recoil-protons generated across. The MST measures the residual energy of recoil-protons downstream of the converter and also discriminates recoil-protons from photons associated to the neutron field. The lay-out of the scintillator/MST system was optimized through an analytical model for selecting the angular range of the scattered protons. The use of unfolding techniques for reconstructing the neutron energy distribution was thus avoided with reasonable uncertainty (about 1.6% in neutron energy) and efficiency (of the order of 10⁻⁶ counts per unit neutron fluence). A semi-empirical procedure was also developed for correcting the non-linearity in light emission from the organic scintillator. The spectrometer was characterized with quasi-monoenergetic and continuous fields of neutrons generated at the CN Van De Graaff accelerator of the INFN-Legnaro National Laboratory, Italy, showing satisfactory agreement with literature data.     

光折变神经网络

本文阐述了几种神经网络模型和模式两分法中感知算法的一种光学执行过程。利用光折变介质动态全息术来存入和修正光学神经网络的加权矩阵。该系统可用以完成模式分类。还介绍了光折变晶体LiNbO₃的一些实验结果。     

Accurate reconstruction of soft X-ray energy spectrum in detector response matrix

In this paper, we report the design of a Monte Carlo simulation for the energy spectrum measurement system based on the ladderabsorption method. Herein, the detector response matrix can be calculated using the detector responses to several monochromatic X-ray beams. A novel soft X-ray spectrum unfolding method based on the two-step reverse iteration(TSRI) algorithm is applied to acquire the primary spectrum. This paper provides examples of the use of TSRI, and the unfolded energy spectra for the soft Xray beams show excellent agreement with the references. The unfolded energy spectra obtained using the TSRI exhibit better accuracy than those obtained from the commonly used unfolding code GRAVEL.     

Identification and classification of organic and inorganic components of particulate matter via Raman spectroscopy and chemometric approaches

In this paper, a novel method for developing a tree‐like classifier which differentiates between organic and inorganic particulate matter by means of Raman spectroscopy is introduced. The algorithm is fully automatic and optimises itself without any human interaction. This method uses a tree‐like structure to classify Raman spectra as a decision tree. On every knot of this tree, the optimal classifier is automatically obtained, tested and trained. The optimal classifier is an artificial neural network, linear discriminant analysis or a support vector machine, where different kernels are possible. The support vector machine is optimised by the simulated annealing method to achieve the best possible classifier. After the training, a hold‐out experiment with two completely independent sets of Raman spectra was tried to show the abilities of this method for real‐world application. Copyright © 2010 John Wiley & Sons, Ltd.     

A fast method for the calculation of electron number density and temperature in laser-induced breakdown spectroscopy plasmas using artificial neural networks

A fast and precise method for the determination of electron temperature and electron number density in laser-induced plasmas is presented. The method is based on the use of a simple artificial neural network (ANN), trained on a suitable set of laser-induced breakdown spectroscopy spectra. The training procedure is quite fast; once the ANN is set, the determination of plasma temperature and electron number density is almost instantaneous, allowing the possibility of measuring these parameters, with good precision, in real time. A direct application of this new method could be the characterization of plasmas generated during pulsed laser deposition process of thin films and nanoparticles generation. The plasma electronic parameters will help to tune the energies involved in the stoichiometry and crystallization control of those nanostructured materials. As an example, the characteristics of the plasma induced by a Nd:YAG laser on a pure titanium target are determined, at different laser fluences.