An algorithms to improve the energy resolution of two-step cascade spectrum

The present paper proposes an algorithm to improve the energy resolution of two-step cascade spectrum. The energy resolution plays an important role in the domain of gamma spectrum analysis. The better the energy resolution is, the better the ability of peak resolving is. The algorithm is constructed based on an analyze of energy resolution of the summation amplitude of coincident pulses spectrometer using the analogue technique. The algorithm proposed has been tested on some two-step cascade spectra of 164Dy nucleus obtained from the (n, ) reaction experiment using the gamma – gamma coincidence spectrometer at Dalat Nuclear Research Institute. Two-step cascade spectra corresponding to the cascade decays from the compound state to final states whose energies are 0, 74, and 242 keV have been evaluated. The results obtained show that the energy resolution of the two-step cascade spectrum has been reduced by 1.05 to 2.04 times within the energy range of 586 to 6830 keV. Our algorithm can therefore be applied to improve the ability of peak deconvolution, the accuracy, and the realibility in analyzing two-step cascade spectra.

Improving the Accuracy of Gamma Spectrum Analysis by Total Variation Based Adaptive Smoothing

Gamma spectrum analysis is an important part of gamma-ray spectroscopy which has been widely used in nuclear engineering, environmental science and astrophysics. As the first step of gamma spectrum analysis, smoothing procedure is critical, since it determines the accuracy of afterwards procedures. Ideally, the smoothing procedure should reduce the statistical fluctuation while preserving characteristic peak information simultaneously. However, current widely-used linear smoothing methods are intrinsically non-adaptive and tend to remove weak peak, which may lose characteristic peak of important radionuclides. To solve the problem, an adaptive smoothing method was proposed to improve the accuracy of gamma spectrum analysis in this study. The proposed method assumes that gamma spectrum is a sparse signal that has meaningful peaks only at limited positions. Based on this assumption, the smoothing procedure is formulated as a nonlinear total variation based optimization problem. Solving this problem promotes the sparsity of gamma spectrum, and therefore reduces meaningless fluctuation, so that the spectrum is adaptively smoothed. The proposed method was applied to gamma spectrum obtained by a Monte Carlo experiment that simulated the ORTEC GEM3070 detector, and compared with traditional linear method. The results demonstrate that the proposed method can effectively reduce the statistical fluctuation of measured gamma spectrum while preserving weak peak much better than standard linear methods. With the proposed method, the accuracy of peak identification and peak calculation is significantly improved.

An Integrated Single-Step Gamma Spectrum Analysis Method Based on Bregmanized Variational Deconvolution

Gamma spectrum analysis is an important technique in many radiation measurement contexts. Current gamma spectrum analysis consists of several separate procedures such as linear smoothing and peak fitting. Although widely used, this routine may give incorrect results when the peak is weak or when several peaks are overlapped together. This problem becomes more severe if the statistical fluctuation of the spectrum is large. To simultaneously resolve overlapping peaks and reduce fluctuations, an integrated gamma spectrum analysis method that can obtain peak information of gamma spectrum in a single step was proposed in this study. The proposed method models both physical modulation of gamma spectrometer and the statistical fluctuations into a single linear equation, and converts the gamma spectrum analysis into an inverse problem. Bregman iteration and total variation regularization are utilized to solve this inverse problem under the framework of variational deconvolution, so that the true spectrum can be directly obtained. The feasibility and performance of the proposed method was evaluated by both numerical simulation experiment and Monte Carlo simulation experiment. The experiment results demonstrate that the proposed method can simultaneously resolve overlapping peak and reduce the fluctuations in gamma spectra while preserves both qualitative and quantitative peak information precisely. The proposed method may provide a new way for the development of gamma spectrum analysis method.

Gamma Spectrum Analysis of Chang’E-1 for Lunar Detection

Gamma-ray spectrum analysis was essential for detecting the elemental abundance and distribution in lunar science. However, for the low-energy region of gamma-ray spectrum, weak peaks were implicated in the fast-decreasing background, and it was difficult to extract characteristic information from original spectra. In order to get a better analytic result, based on wavelet and FFT filtering methods in frequency domain, we had processed the gamma-ray spectrometer (GRS) data of Chang’E-1 (CE-1), and well extracted some useful information of spectral characteristic peaks. Then we preliminarily mapped the distribution of net peak counts for potassium on lunar surface, which indirectly reflected the distribution of elemental abundance. At last, we compared our analytic result with that of Apollo and Lunar Prospector (LP), and found some consistencies and differences.