Applications of the non-negative least-squares deconvolution method to analyze energy-dispersive x-ray fluorescence spectra.

We used the Monte Carlo simulation method to establish a detector response matrix and the non-negative least-squares method to deconvolute x-ray spectra. The simulation and experimental data verified the effectiveness of this method, and the influence of full-width at the half of the maximum calibration accuracy on the deconvolution results was investigated. The non-negative least-squares method had high accuracy and efficiency compared with others. The results showed that, except for Zn, the relative errors between the inversion and the standard values were less than 0.1% for the simulated spectra. For the experimental data, the relative errors were within 0.2%. The peaks with similar characteristic energies can be better distinguished in the deconvolution spectra, reducing the errors caused by overlapping peaks in subsequent analysis.

A New Temperature Correction Method for NaI(Tl) Detectors Based on Pulse Deconvolution.

To overcome the temperature effect of NaI(Tl) detectors for energy spectrometry without additional hardware, a new correction method was put forward based on pulse deconvolution, trapezoidal shaping and amplitude correction, named DTSAC. To verify this method, actual pulses from a NaI(Tl)-PMT detector were measured at various temperatures from -20 °C to 50 °C. Pulse processing and spectrum synthesis showed that the position drift of the 137Cs 662 keV peak was less than 3 keV, and the corresponding resolution at 662 keV of the sum spectra ranged from 6.91% to 10.60% with the trapezoidal width set from 1000 ns to 100 ns. The DTSAC method corrects the temperature effect via pulse processing, and needs no reference peak, reference spectrum or additional circuits. The method solves the problem of correction of pulse shape and pulse amplitude at the same time, and can be used even at a high counting rate.

Neutron spectrometry of D2O-moderated 252Cf with Bonner sphere spectrometer.

For neutron spectrometry of the D2O-moderated 252Cf source with a Bonner sphere spectrometer (BSS), it is difficult to use the large and heavy shadow cone to correct the neutron scattering effect. To overcome this problem, Monte Carlo (MC) simulation method was applied to calculate the neutron scattering ratio and to establish the BSS response functions. The simulated response functions were verified by experimental measurements in reference mono-energetic neutron fields. MC simulation based scattering-correction was validated by measurement of 252Cf neutron field. The measured and simulated values of the neutron scattering ratio were very close with relative errors within ±6%. Finally, the neutron spectrum and the spectrum averaged conversion coefficients of the D2O-moderated 252Cf were measured using BSS after scattering-correction by MC simulation, and the results agreed with the values recommended by ISO 8529-1:2021. It shows that the MC simulation can be a useful substitute to shadow cones method for neutron scattering-correction.

Comparison of machine learning approaches for radioisotope identification using NaI(TI) gamma-ray spectrum.

This research aims at comparing the performance of different machine learning algorithms used for NaI(TI) gamma-ray detector based radioisotope identification. Six machine learning algorithms were implemented, including support vector machine (SVM), k-nearest neighbor (KNN), logistic regression (LR), naive Bayes (NB), decision tree (DT), and multilayer perceptron (MLP). The hyper-parameters of each model were elaborately optimized. The effects of data size, statistical fluctuation, and spectrum drift were considered. Results show that for smaller data size (5 types of radioisotopes and 6000 spectra), the support vector machine and the logistic regression classifier exhibit higher identification accuracy with less training/predicting time. Whereas for larger data size (14 types of radioisotopes corresponding to the standard IEC 62327-2017), the multilayer perceptron showed highest accuracy but required the longest time for model training. The naive Bayes classifier and the decision tree were prone to make mistakes when fluctuations and distortions were added to the spectra. The k-nearest neighbor classifier, though showing high accuracy for most data sets, consumed the longest time while making prediction.

Gamma spectrum stabilization method based on nonlinear least squares optimization.

To suppress spectral drift in lengthy gamma spectrum measurements, a convenient and practical spectrum stabilization method with software-only adjustment is studied. The gamma energy spectra are recorded in consecutive small time intervals. The drifts in different time intervals relative to the first reference spectrum are estimated with a full spectrum nonlinear optimization technique, and the drifted spectra are corrected and accumulated to form a drift-free spectrum. This method needs neither hardware adjustment such as high voltage or electronics gain, nor peak location. Presented experimental results show that the stabilization accuracy can be significantly improved using this approach.

PIN silicon diode fast neutron detector.

Two batches of diodes, with different structural ratios (the ratio of area and thickness), were made using different manufacturing processes. The energy response of the first batch to 15 kinds of monoenergetic neutrons ranging from 180 keV to 17.56 MeV was tested, and the neutron source response of both batches to 239Pu-Be neutron source was measured. The energy deposition in the diodes irradiated by 1 keV to 20 MeV monoenergetic neutrons was calculated with simulation procedure. The response curve of the experimental results showed an approximately similar trend to that of theoretical computation. Based on the results of the neutron source response experiments, it was concluded that the response of fast neutron varied linearly with the structural ratio of the detectors.