Abstract
Gabbard et al. have demonstrated that convolutional neural networks can achieve the sensitivity of matched filtering in the recognization of the gravitational-wave signals with high efficiency [Phys. Rev. Lett. 120, 141103 (2018)]. In this work we show that their model can be optimized for better accuracy. The convolutional neural networks typically have alternating convolutional layers and max pooling layers, followed by a small number of fully connected layers. We increase the stride in the max pooling layer by 1, followed by a dropout layer to alleviate overfitting in the original model. We find that these optimizations can effectively increase the area under the receiver operating characteristic curve for various tests on the same dataset.
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Acknowledgements
We thank the reviewers for providing constructive comments and suggestions to improve the quality of this paper. W. L. was supported by grants from NSFC (Grant Nos. 11647314 and 11847307). Q. G. H. was supported by grants from NSFC (Grant Nos. 11690021, 11575271, and 11747601), the Strategic Priority Research Program of Chinese Academy of Sciences (Grant Nos. XDB23000000 and XDA15020701), as well as Top-Notch Young Talents Program of China. This research has made use of data, software and/or web tools obtained from the Gravitational Wave Open Science Center (https://www.gw-openscience.org), a service of LIGO Laboratory, the LIGO Scientific Collaboration and the Virgo Collaboration.
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Luo, HM., Lin, W., Chen, ZC. et al. Extraction of gravitational wave signals with optimized convolutional neural network. Front. Phys. 15, 14601 (2020). https://doi.org/10.1007/s11467-019-0936-x
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DOI: https://doi.org/10.1007/s11467-019-0936-x