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Abstract
We developed a multi-tasking deep learning model for simultaneous pulse height estimation and pulse shape discrimination for pile-up n/γ signals. Compared with single-tasking models, our model showed better spectral correction performance with higher recall for neutrons. Further, it achieved more stable neutron counting with less signal loss and a lower error rate in the predicted gamma ray spectra. Our model can be applied to a dual radiation scintillation detector to discriminatively reconstruct each radiation spectrum for radioisotope identification and quantitative analysis.

Affiliations

Junhyeok Kim 1, Byoungil Jeon 2, Jisung Hwang 1, Gyohyeok Song 1, Myungkook Moon 3, Gyuseong Cho 4
1Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea.
2Artificial Intelligence Application & Strategy Team, Korea Atomic Energy Research Institute, Daejeon, 34507, Republic of Korea.
3Neutron Science Division, Korea Atomic Energy Research Institute, Daejeon, 34507, Republic of Korea. Electronic address: moonmk@kaeri.re.kr.
4Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea. Electronic address: gscho@kaist.ac.kr.