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Some optimizations on detecting gravitational wave using convolutional neural network

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Abstract

This work investigates the problem of detecting gravitational wave (GW) events based on simulated damped sinusoid signals contaminated with white Gaussian noise. It is treated as a classification problem with one class for the interesting events. The proposed scheme consists of the following two successive steps: decomposing the data using a wavelet packet, representing the GW signal and noise using the derived decomposition coeficients; and determining the existence of any GW event using a convolutional neural network (CNN) with a logistic regression output layer. The characteristic of this work is its comprehensive investigations on CNN structure, detection window width, data resolution, wavelet packet decomposition and detection window overlap scheme. Extensive simulation experiments show excellent performances for reliable detection of signals with a range of GW model parameters and signal-to-noise ratios. While we use a simple waveform model in this study, we expect the method to be particularly valuable when the potential GW shapes are too complex to be characterized with a template bank.

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Acknowledgements

Authors thank H. Gabbard, M. Williams, F. Hayes, and C. Messenger very much for their enthusiastic discussions and sharing of their experimental code. We are grateful for valuable suggestions and corrections from anonymous reviewers, Eric D. Feigelson, Dr. Jin Li and B. S. Sathyaprakash. X. L. and W. Y. were supported by the National Natural Science Foundation of China (Grant Nos. 11973022 and U1811464), the Natural Science Foundation of Guangdong Province (No. 2020A1515010710), and China Scholarship Council (No. 201706755006), and the Joint Research Fund in Astronomy (No. U1531242) under cooperative agreement between the National Natural Science Foundation of China (NSFC) and Chinese Academy of Sciences (CAS). Xilong Fan was supported by the National Natural Science Foundation of China (Grant Nos. 11673008 and 11922303) and Hubei Province Natural Science Fund for the Distinguished Young Scholars.

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Authors and Affiliations

  1. School of Computer Science, South China Normal University, Guangzhou, 510631, China

    Xiang-Ru Li

  2. School of Mathematical Sciences, South China Normal University, Guangzhou, 510631, China

    Wo-Liang Yu

  3. School of Physics and Technology, Wuhan University, Wuhan, 430072, China

    Xi-Long Fan

  4. Pennsylvania State University, University Park, PA, 16802, USA

    G. Jogesh Babu

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  1. Xiang-Ru Li
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  2. Wo-Liang Yu
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Correspondence to Xiang-Ru Li or Xi-Long Fan.

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Li, XR., Yu, WL., Fan, XL. et al. Some optimizations on detecting gravitational wave using convolutional neural network. Front. Phys. 15, 54501 (2020). https://doi.org/10.1007/s11467-020-0966-4

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