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Some optimizations on detecting gravitational wave using convolutional neural network
Frontiers of Physics ( IF 7.5 ) Pub Date : 2020-06-10 , DOI: 10.1007/s11467-020-0966-4
Xiang-Ru Li , Wo-Liang Yu , Xi-Long Fan , G. Jogesh Babu

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.

中文翻译:

卷积神经网络检测重力波的一些优化

这项工作研究了基于模拟高斯白噪声的阻尼正弦信号检测重力波(GW)事件的问题。它被视为一个有趣问题的分类问题。所提出的方案包括以下两个连续步骤:使用小波包分解数据,使用导出的分解系数表示GW信号和噪声;并使用具有逻辑回归输出层的卷积神经网络(CNN)确定是否存在任何GW事件。这项工作的特点是对CNN结构,检测窗口宽度,数据分辨率,小波包分解和检测窗口重叠方案进行了全面研究。大量的仿真实验显示了出色的性能,可以可靠地检测各种GW模型参数和信噪比的信号。尽管我们在这项研究中使用了简单的波形模型,但是当潜在的GW形状太复杂而无法用模板库进行表征时,我们希望该方法特别有价值。
更新日期:2020-06-10
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