Chinese Physics C > 2016, Vol. 40 > Issue(2): 026202 DOI: 10.1088/1674-1137/40/2/026202

Improvement of algorithms for digital real-time n-γdiscrimination

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Xi'an Research Institute of Hi-Tech, Xi'an 710025, China

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Abstract：
Three algorithms (the Charge Comparison Method, n- γModel Analysis and the Centroid Algorithm) have been revised to improve their accuracy and broaden the scope of applications to real-time digital n- γdiscrimination. To evaluate the feasibility of the revised algorithms, a comparison between the improved and original versions of each is presented. To select an optimal real-time discrimination algorithm from these six algorithms (improved and original), the figure-of-merit (FOM), Peak-Threshold Ratio (PTR), Error Probability (EP) and Simulation Time (ST) for each were calculated to obtain a quantitatively comprehensive assessment of their performance. The results demonstrate that the improved algorithms have a higher accuracy, with an average improvement of 10% in FOM, 95% in PTR and 25% in EP, but all the STs are increased. Finally, the Adjustable Centroid Algorithm (ACA) is selected as the optimal algorithm for real-time digital n- γdiscrimination.
- n- discrimination,
- improvement of algorithms,
- real-time discrimination

References

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Song Wang, Peng Xu, Chang-Bing Lu, Yong-Gang Huo and Jun-Jie Zhang. Improvement of algorithms for digital real-time n- γdiscrimination[J]. Chinese Physics C, 2016, 40(2): 026202. doi: 10.1088/1674-1137/40/2/026202

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Received: 2014-11-20
Revised: 2015-09-23

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通讯作者:陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Improvement of algorithms for digital real-time n-γdiscrimination

1. Xi'an Research Institute of Hi-Tech, Xi'an 710025, China

Received Date:2014-11-20

Accepted Date:2015-09-23

Available Online:2016-02-20

Abstract:Three algorithms (the Charge Comparison Method, n-γModel Analysis and the Centroid Algorithm) have been revised to improve their accuracy and broaden the scope of applications to real-time digital n-γdiscrimination. To evaluate the feasibility of the revised algorithms, a comparison between the improved and original versions of each is presented. To select an optimal real-time discrimination algorithm from these six algorithms (improved and original), the figure-of-merit (FOM), Peak-Threshold Ratio (PTR), Error Probability (EP) and Simulation Time (ST) for each were calculated to obtain a quantitatively comprehensive assessment of their performance. The results demonstrate that the improved algorithms have a higher accuracy, with an average improvement of 10% in FOM, 95% in PTR and 25% in EP, but all the STs are increased. Finally, the Adjustable Centroid Algorithm (ACA) is selected as the optimal algorithm for real-time digital n-γdiscrimination.

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Improvement of algorithms for digital real-time n-γdiscrimination

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