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.
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References
Q. Yue, W.P. Lai, W.C. Chang et al., Effective dynamic range in measurements with flash analog-to-digital convertor. Nucl. Instrum. Methods Phys. Res. Sect. A 511, 408–416 (2003). https://doi.org/10.1016/s0168-9002(03)02020-5
X.B. Xie, Recover certain low-frequency information for full waveform inversion. SEG Tech. Prog. Expand. Abstr. 2013, 1053–1057 (2019). https://doi.org/10.1190/segam2013-0451.1
D.W. Huang, J.H. Yang, D.J. Zhou et al., Recovering an unknown signal completely submerged in strong noise by a new stochastic resonance method. Commun. Nonlinear Sci. 66, 156–166 (2019). https://doi.org/10.1016/j.cnsns.2018.06.011
S. Marrone, D. Cano-Ott, N. Colonna et al., Pulse shape analysis of liquid scintillators for neutron studies. Nucl. Instrum. Methods Phys. Res. Sect. A 490, 299–307 (2002). https://doi.org/10.1016/s0168-9002(02)01063-x
M. Cavallaro, S. Tropea, C. Agodi et al., Pulse-shape discrimination in NE213 liquid scintillator detectors. Nucl. Instrum. Methods Phys. Res. Sect. A 700, 65–69 (2013). https://doi.org/10.1016/j.nima.2012.10.056
X.Z. Liu, S.B. Liu, Q. An, A time-over-threshold technique for PMT signals processing. Nucl. Sci. Tech. 18, 164–171 (2007). https://doi.org/10.1016/S1001-8042(07)60040-2
C.B. Pushpalatha, Multivariate analysis methods in particle physics. Annu. Rev. Nucl. Part. Sci. 61, 281–309 (2011). https://doi.org/10.1146/annurev.nucl.012809.104427
M.G. Bonelli, M. Ferrini, A. Manni, Artificial neural networks to evaluate organic and in organic contamination in agricultural soils. Chemosphere 186, 124–131 (2017). https://doi.org/10.1016/j.chemosphere.2017.07.116
G. Liu, M.D. Aspinall, X. Ma et al., An investigation of the digital discrimination of neutrons and gamma rays with organic scintillation detectors using an artificial neural network. Nucl. Instrum. Methods A 607(3), 620–628 (2009). https://doi.org/10.1016/j.nima.2009.06.027
J.P. He, X.B. Tang, P. Gong et al., Spectrometry analysis based on approximation coefficients and deep belief networks. Nucl. Sci. Tech. 29, 69 (2018). https://doi.org/10.1007/s41365-018-0402-4
Q.J. Zhu, L.C. Tian, X.H. Yang et al., Advantages of artificial neural network in neutron spectra unfolding. Chin. Phys. Lett. 31(7), 256–307 (2014). https://doi.org/10.1088/0256-307X/31/7/072901
S.A. Hosseini, Neutron spectrum unfolding using artificial neural network and modified least square method. Radiat. Phys. Chem. 126, 75–84 (2016). https://doi.org/10.1016/j.radphyschem.2016.05.010
H. Qiao, C.Y. Lu, X. Chen et al., Signal-background discrimination with convolutional neural networks in the Panda X-III experiment using MC simulation. Sci. China Phys. Mech. 61, 101007 (2018). https://doi.org/10.1007/s11433-018-9233-5
A. Yadollahi, E. Nazemi, A. Zolfaghari et al., Optimization of thermal neutron shield concrete mixture using artificial neural network. Nucl. Eng. Des. 305, 146–155 (2016). https://doi.org/10.1016/j.nucengdes.2016.05.012
H. Gabbard, M. Williams, F. Hayes et al., Matching matched filtering with deep networks for gravitational-wave astronomy. Phys. Rev. Lett. 120, 141103 (2018). https://doi.org/10.1103/PhysRevLett.120.141103
D.L. Deng, Machine learning detection of bell nonlocality in quantum many-body systems. Phys. Rev. Lett. 120, 240402 (2018). https://doi.org/10.1103/PhysRevLett.120.240402
X.G. Tuo, B. Cheng, K.L. Mu et al., Neural network-based matrix effect correction in EDXRF analysis. Nucl. Sci. Tech. 19(5), 278–281 (2008). https://doi.org/10.1016/S1001-8042(09)60004-X
L. Zou, L.C. Wang, L. Xia et al., Prediction and comparison of solar radiation using improved empirical models and adaptive neuro-fuzzy inference systems. Renew. Energy 106, 343–353 (2017). https://doi.org/10.1016/j.renene.2017.01.042
Eljen Technology, Sweetwater TX79556 USA. http://www.ggg-tech.co.jp/maker/eljen/ej-331.html
S.T. Lin, Q. Yue, Status and prospects of CJPL and the CDEX experiment. Phys. Procedia 61, 201–204 (2015). https://doi.org/10.1016/j.phpro.2014.12.032
H.Y. Xing, X.Z. Yu, J.J. Zhu et al., Simulation study of the neutron–gamma discrimination capability of a liquid scintillator neutron detector. Nucl. Instrum. Methods Phys. Res. Sect. A 768, 1–8 (2014). https://doi.org/10.1016/j.nima.2014.08.049
M.G. Bonelli, M. Ferrini, A. Manni, Artificial neural networks to evaluate organic and in organic contamination in agricultural soils. Chemosphere 186, 124–131 (2017). https://doi.org/10.1016/j.chemosphere.2017.07.116
P.S. Sastry, G. Santharam, K.P. Unnikrishnan, Memory neuron networks for identification and control of dynamic systems. IEEE Trans. Neural Netw. 5, 306–319 (1994). https://doi.org/10.1109/72.279193
H. Yu, T. Xie, S. Paszczynski et al., Advantages of radial basis function networks for dynamic system design. IEEE Trans. Ind. Electron. 58, 5438–5450 (2011). https://doi.org/10.1109/TIE.2011.2164773
Y.M. Wu, Y.Q. Wang, L. Li, Application study on BP network and generalized RBF network in estimating distribution model of mechanical products. Chin. J. Mech. Eng. 20, 2140–2144 (2006). https://doi.org/10.1007/11816157_5 (in Chinese)
B.M. Vaziri, A. Shahsavand, Analysis of supersonic separators geometry using generalized radial basis function (GRBF) artificial neural networks. J. Nat. Gas Sci. Eng. 13, 30–41 (2013). https://doi.org/10.1016/j.jngse.2013.03.004
Matlab, The Math Works, Inc., http://www.mathworks.com
C.B. Pushpalatha, Multivariate analysis methods in particle physics. Ann. Rev. Nucl. Part. Sci. 61, 281–309 (2011). https://doi.org/10.1146/annurev.nucl.012809.104427
Y. Zhang, M.J. Er, R. Zhao et al., Multiview convolutional neural networks for multi document extractive summarization. IEEE Trans. Cybern. 47(10), 3230–3242 (2017). https://doi.org/10.1109/TCYB.2016.2628402
F.G. Zhao, J. Chen, L. Gou et al., Neuro-fuzzy based condition prediction of bearing health. J. Vib. Control 15(7), 1079–1091 (2009). https://doi.org/10.1177/1077546309102665
Y. Wu, B.B. Zhang, J.B. Lu et al., Fuzzy logic and neuro-fuzzy systems: a systematic introduction. Int. J. Artif. Intell. Expert Syst. 2(2), 47–80 (2011)
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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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Liu, Y., Zhu, JJ., Roberts, N. et al. Recovery of saturated signal waveform acquired from high-energy particles with artificial neural networks. NUCL SCI TECH 30, 148 (2019). https://doi.org/10.1007/s41365-019-0677-0
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DOI: https://doi.org/10.1007/s41365-019-0677-0