Application of deconvolution based pattern recognition algorithm for identification of rings in spectra from RICH detectors

To obtain the characteristic information of unknown radionuclides by analyzing the γ-energy spectrum of a low-resolution detector, and to improve the accuracy and validity of the analysis of overlapping and weak peaks in the γ-energy spectrum, this paper carried out a study on the γ-energy spectrum analysis of NaI(Tl) detectors based on the Boosted-Gold algorithm. A simulation model of NaI(TI) detector was established by using MCNPX, and a detector response matrix of dimension 201×200 was obtained. The γ-energy spectrum unfolding program was developed based on the Boosted-Gold algorithm. The detector response spectra of the γ radioactive sources 22Na, 133Ba, and 152Eu were measured, and three groups low-resolution γ spectra were constructed with different γ-ray energies, different energy differences (?E) and different relative intensities by simulation, and combined with the response matrix and the unfolding procedures to unfold the measured and simulated γ energy spectra. The unfolding results were analyzed with the nuclide standard characteristics information from the IAEA database. The results show that the maximum unfolding error of the characteristic energy of the measured γ-energy spectrum is 2.17% (0.276 MeV for 133Ba source) by the Boosted-Gold algorithm, and the maximum deviation between the unfolded intensity and the standard intensity is 0.197 (1.408 MeV for 152Eu source). For the simulated γ energy spectrum, the characteristic energy of nuclide can be accurately analyzed, and the deviations of unfolded intensity and standard intensity remain within 0.01; When the enhancement factor p≤14, the Boosted-Gold algorithm is beneficial to the quantitative analysis of γ-radionuclides; For the relative intensity of γ-rays greater than 10%, this algorithm has better analysis accuracy.

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Application of deconvolution based pattern recognition algorithm for identification of rings in spectra from RICH detectors

Cited by 3 publications

References 21 publications

Method of identification of rings in spectra from RICH detectors using a deconvolution-based pattern recognition algorithm

Method of identification of rings in spectra from RICH detectors using a deconvolution-based pattern recognition algorithm

Nonnegative constraint quadratic program technique to enhance the resolution of γ spectra

Analysis of the unfolded γ energy spectrum based on Boosted-Gold algorithm

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