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Abstract
Liquid scintillation counters are common instruments used in the measurement of pure beta-emitting radionuclides, and while they represent a conventional radiometric technique, they are still competitive for their potential to measure multiple radionuclides simultaneously. In this work, we propose an algorithm based on an artificial neural network (ANN) for the simultaneous analysis of the beta-ray spectra of 3H and 14C in dual beta-labeled samples using a liquid scintillation counter. We achieved percentage deviations below 5.0% using the proposed algorithm in 16 out of 18 cases, with RMSDs below 1.5% in 17 out of 18 cases. The trained ANN also produced activity ratios with high accuracy even while having to deal with highly fluctuating spectra. Results demonstrate that the rapid predictions with a short measurement time from our proposed ANN method are compatible with the calculated ones from previous studies that were obtained with long measurement times.

Affiliations

Sungyeop Joung  1 , Yewon Kim  2 , Jinhwan Kim  2 , Jiyoung Park  3 , Mee Jang  3 , Jinhyung Lee  3 , Chang-Joung Kim  3 , Min Sun Lee  3 , Jong-Myoung Lim  3
1 Environment and Disaster Assessment Research Division, Korea Atomic Energy Research Institute. Electronic address: jsy1003@kaeri.re.kr.
2 Department of Nuclear & Quantum Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea.
3 Environmental Radioactivity Assessment Team, Korea Atomic Energy Research Institute (KAERI), 111, Daedeok-Daero 989, Yuseong-Gu, Daejeon, Republic of Korea.