作者：滕忠斌, 宋明哲, 倪宁, 魏可新, 刘蕴韬
Study on unfolding method of low-energy filtered X-ray spectrum using artificial neural network
TENG Zhongbin, SONG Mingzhe, NI Ning, WEI Kexin, LIU Yuntao
Radiation Metrology Department of China Institute of Atomic Energy, Beijing 102413, China
Abstract: The artificial neural network was used to unfold the low-energy filtered X-ray pulse height spectra measured by a PIPS detector efficiently. Based on the computed tomography image of the PIPS detector, the Monte Carlo (MC) model of the PIPS detector was built in MCNP5. By performing the experiments efficiency calibration, MC efficiency calibration and the comparison of the measured and simulated pulse height spectrum, the MC model of the detector was verified. Then the MC model was used to calculate the response functions of the PIPS detector for the mono-energetic photons (5-30 keV). Then response functions were used as the training data of the neural network. The measured X-ray pulse height spectra of the N10-N30 and L10-L30 radiation qualities were unfolded to the true fluence spectra, and it were compared with the unfolded spectra using the GRV_MC33 program. The results show that except for N25 and N30 radiation quality, the results of the two method are in good agreement, which verifies the feasibility of using neural networks to unfold the low-energy X-ray pulse height spectrum. The difference in the calculated spectrum possibly come from that the uncertainties of the response functions and the unfolding method of the GRV_MC33 code. Ultimately, the trained neural network can be transplanted in a minicomputer and help calibration laboratories achieve the fast unfolding of the low-energy filtered X-rays.
Keywords: low-energy filtered X-rays;unfolding of pulse height spectra;PIPS detector;efficiency calibration;artificial neural network
2021, 47(2):26-31 收稿日期: 2020-06-23;收到修改稿日期: 2020-08-02
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