Nuclear Science and Techniques

《核技术》(英文版) ISSN 1001-8042 CN 31-1559/TL     2019 Impact factor 1.556

Nuclear Science and Techniques ›› 2019, Vol. 30 ›› Issue (10): 148 doi: 10.1007/s41365-019-0677-0


Recovery of saturated signal waveform acquired in high energy particle with artificial neural networks

Yu Liu1 Jing-Jun Zhu2 Neil Roberts3 Ke-Ming Chen1 Yu-Lu Yan1 Shuang-Rong Mo2 Peng Gu1 Hao-Yang Xing1   

  1. 1School of Physical Science and Technology, Sichuan University, Chengdu 610064, China
    2 Key Laboratory of Radiation Physics and Technology of Ministry of Education, Institute of Nuclear Science and Technology, Sichuan University, Chengdu 610065, China
    3Edinburgh Imaging, School of Clinical Sciences, University of Edinburgh, United Kingdom
  • Received:2019-02-01 Revised:2019-05-02 Accepted:2019-05-09
  • Contact: Hao-Yang Xing
  • Supported by:
    This work is supported by the “Detection of very low-flux background neutrons in China Jinping Underground Laboratory” project of the National Natural Science Foundation of China (No.11275134).
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Yu Liu, Jing-Jun Zhu, Neil Roberts, Ke-Ming Chen, Yu-Lu Yan, Shuang-Rong Mo, Peng Gu, Hao-Yang Xing. Recovery of saturated signal waveform acquired in high energy particle with artificial neural networks.Nuclear Science and Techniques, 2019, 30(10): 148     doi: 10.1007/s41365-019-0677-0

Abstract: Artificial neural networks (ANNs) are a core component of artificial intelligence and are frequently used in machine learning. In this report, we investigate the use of ANNs to recover the saturated signals acquired in high-energy particle and nuclear physics experiments. The inherent properties of the detector and hardware imply that particles with relatively high energies probably often generate saturated signals. Usually, these saturated signals are discarded during data processing, and therefore, some useful information is lost. Thus, it is worth restoring the saturated signals to their normal form. The mapping from a saturated signal waveform to a normal signal waveform constitutes a regression problem. Given that the scintillator and collection usually do not form a linear system, typical regression methods such as multi-parameter fitting are not immediately applicable. One important advantage of ANNs is their capability to process nonlinear regression problems. To recover the saturated signal, three typical ANNs were tested including backpropagation (BP), simple recurrent (Elman), and generalized radial basis function (GRBF) neural networks (NNs). They represent a basic network structure, a network structure with feedback, and a network structure with a kernel function, respectively. The saturated waveforms were produced mainly by the environmental gamma in a liquid scintillation detector for the China Dark Matter Detection Experiment (CDEX). The training and test data sets consisted of 6000 and 3000 recordings of background radiation, respectively, in which saturation was simulated by truncating each waveform at 40% of the maximum signal. The results show that the GBRF-NN performed best as measured using a Chi-squared test to compare the original and reconstructed signals in the region in which saturation was simulated. A comparison of the original and reconstructed signals in this region shows that the GBRF neural network produced the best performance. This ANN demonstrates a powerful efficacy in terms of solving the saturation recovery problem. The proposed method outlines new ideas and possibilities for the recovery of saturated signals in high-energy particle and nuclear physics experiments. This study also illustrates an innovative application of machine learning in the analysis of experimental data in particle physics.

Key words: Saturated signals, Artificial Neural Networks (ANNs), Recovery of signal waveform, Generalized radial basis function, Backpropagation neural network, Elman neural network