Weak information extraction of gamma spectrum based on a two-dimensional wavelet transform

This paper studies the spectrum of gamma rays based on two-dimensional wavelet transforms using Haar, Daubechies, and Symlets wavelets. A two-level two-dimensional transform is carried out using these three wavelets. The paper focuses on the amplitude and extreme horizontal components of high-frequency components after each stage of transform and extracts the weak information from the gamma-ray spectrum data. This paper analyzes three wavelets, two moments, and two energy levels. The analysis incorporates a small support torque and a two-dimensional transform. The standard traversal comparison peak search approach, the symmetric zero area method, the Gaussian fitting method, and the contemporary popular SNIP algorithms are also compared and analyzed in this work. The outcomes were also contrasted with the NaI(Tl) detector's measured peak levels that were calibrated in a lab. The analysis's findings demonstrate that, in the presence of high radioactive back-ground, standard analysis techniques are unable to assess weak information. The chosen Daubechies and Symlets wavelets were used for the first-level 2D wavelet transform processing, and their peak search results surpassed those obtained from SNIP-processed analysis. Additionally, the standard deviation approach was applied to the outcomes of the 2D wavelet and SNIP transform to identify transform fluctuations in the local peaks of the complete spectrum and assess the algorithm's stability. The outcomes demonstrate that the local peaks of the 2D Symlets wavelet transform and 2D Daubechies transform, as well as the full spectrum fluctuations, are very tiny and steady. This technology can extract weak information from the data and can be applied to other gamma-ray data sets. It lays the foundation for a shielding study of special radiation protection materials.