Weak peak identification of gamma spectrum based on singular value decomposition

BackgroundWhen performing gamma-ray spectroscopy analysis of samples with low levels of radioactive nuclide content, the weak peaks are difficult to be identified.PurposeThis study aims to propose a new method for identifying peaks in γ spectra by utilizing singular value decomposition (SVD) to improve the detection efficiency of weak peaks.MethodsFirstly, the matrix construction of spectrum data was improved by transforming the γ spectrum into a two-way cyclic matrix, and singular value decomposition of matrix was performed to get singular values and singular vectors. Then, the second singular value was selected to reconstruct the matrix and perform peak finding. Finally, the γ spectrum of the radioactive source 152Eu was used as the experimental object, the peak finding performance of proposed method was compared with that of first-order derivative peak finding, symmetric zero-area peak finding, and singular value decomposition peak finding.ResultsComparison result show that the bidirectional circular matrix SVD peaking method has higher recall rate, precision rate, and F1 value, achieving 100%, 87% and 0.94, respectively.ConclusionsThe approach of this study can optimize weak peak detection and offer additional options for peak finding methods.