https://doi.org/10.1140/epjp/s13360-020-00100-9
Regular Article
Applications of non-negative iterative deconvolution method in the analysis of alpha-particle spectra
1
School of Computer Science and Engineering, Sichuan University of Science and Engineering, Zigong, 643000, China
2
Key Laboratory of Higher Education of Sichuan Province for Enterprise Informationalization and Internet of Things, Sichuan University of Science and Engineering, Zigong, 643000, China
3
School of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu, 610059, China
* e-mail: tuoxg@cdut.edu.cn
Received:
4
July
2019
Accepted:
3
January
2020
Published online:
6
February
2020
Different from the regular method with peak-shape function fitting, in this work, the idea of alpha-spectra analysis using non-negative iterative deconvolution method is proposed. Two non-negative iterative algorithms, the Boosted Gold and the Boosted Richardson–Lucy, were applied to unfold and analyze alpha-particle spectra. The detector response matrix was constructed with the AASI, which is a Monte Carlo simulation software specifically for alpha-particle spectra. A 
Cm alpha-particle source was measured and tested for peak-position deconvolution. A 
Pu alpha-particle spectrum was used to prove the validity of quantitative analysis by deconvolution algorithms. Both the Boosted Gold and the Boosted Richardson–Lucy can get accurate results for peak positions and content ratios. In terms of running speed, the Boosted Gold was faster than the Boosted Richardson–Lucy.
© Società Italiana di Fisica (SIF) and Springer-Verlag GmbH Germany, part of Springer Nature, 2020
