Statistics distributed gamma or X ray energy spectrum unscrambling method

Abstract

The invention discloses a statistics distributed gamma or X ray energy spectrum unscrambling method which solves the problems of poor generality, large workload and poor precision of the traditional method. According to the invention, aiming at a Gaussian part in a gamma or X ray full-energy peak, a plurality of parameters with respective physical significances, which are obtained according to gamma or X ray detection characteristics are established, and solution processes of the parameters are illustrated. The statistics distributed gamma or X ray energy spectrum unscrambling method is technically characterized in that under the condition of stable or unchangeable simulation conditions of a gamma or X ray detector, the parameters are figured out once, and energy spectrum fitting can be carried out form a long time by using the parameters, so that the unscrambling realizing process is simplified and universalized, the defects of variable parameters and insufficient significance in the traditional energy spectrum fitting function or simulation energy spectrum widening function unscrambling method are avoided, and the analysis speed and the analysis precision of gamma or X ray energy spectrum analysis are improved.

Description

Technical field: [0001] The invention relates to a statistically distributed gamma or X-ray energy spectrum analysis method based on a normal distribution probability function in the process of nuclear radiation energy spectrum measurement and analysis. Background technique: [0002] When nuclear radiation detectors are used to measure the energy spectrum of γ or X-rays, it is common to see the phenomenon that the spectral peaks overlap each other. In order to obtain the information of γ or X-rays under a single energy in the measured γ or X-ray energy spectrum, the corresponding γ or The energy and intensity of X-rays must be analyzed by a certain spectrum resolution method to analyze the measured energy spectrum. [0003] For a long time, researchers at home and abroad have devoted themselves to the research of function fitting spectrum analysis methods, that is, using the least squares method or partial least squares method to perform function fitting on the measured poin...

AI Technical Summary

Problems solved by technology

The energy spectrum broadening method in MCNP uses the parameter FWHM for fitting instead of the Gaussian full-energy peak standard deviation σ, so the fitting accuracy is not high enough. Broadening, and then performing spectrum analysis on the broadened analog spectrum, the same as the above-mentioned Gaussian function spectrum decomposition method, each measured energy spectrum is fitted separately, and the undetermined coefficient values ​​of the fitting parameters are all different, which increases the workload. , which limits the generality of the spectral solution method

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