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Extraction of gravitational wave signals with optimized convolutional neural network |
Hua-Mei Luo1( ), Wenbin Lin2,3(), Zu-Cheng Chen4,5(), Qing-Guo Huang4,5() |
1. School of Mathematics, Southwest Jiaotong University, Chengdu 610031, China
2. School of Physical Science and Technology, Southwest Jiaotong University, Chengdu 610031, China
3. School of Mathematics and Physics, University of South China, Hengyang 421001, China
4. CAS Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China
5. School of Physical Sciences, University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China |
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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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| Keywords
gravitational wave
convolutional neural networks
deep learning
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Corresponding Author(s):
Hua-Mei Luo,Wenbin Lin,Zu-Cheng Chen,Qing-Guo Huang
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Issue Date: 22 November 2019
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| 1 |
B. P. Abbott, et al. (LIGO Scientific Collaboration, Virgo Collaboration), Observation of gravitational waves from a binary black hole merger, Phys. Rev. Lett. 116, 061102 (2016), arXiv: 1602.03837 [gr-qc]
|
| 2 |
B. P. Abbott, et al. (LIGO Scientific Collaboration, Virgo Collaboration), GW151226: Observation of gravitational waves from a 22-solar-mass binary black hole coalescence, Phys. Rev. Lett. 116, 241103 (2016), arXiv: 1606.04855 [gr-qc]
|
| 3 |
B. P. Abbott, et al. (LIGO Scientific Collaboration, Virgo Collaboration), GW170104: Observation of a 50-solar mass binary black hole coalescence at redshift 0.2, Phys. Rev. Lett. 118, 221101 (2017), [Erratum: Phys. Rev. Lett. 121 (12), 129901 (2018)], arXiv: 1706.01812 [gr-qc]
|
| 4 |
B. P. Abbott, et al. (LIGO Scientific Collaboration, Virgo Collaboration), GW170608: Observation of a 19-solarmass Binary Black Hole Coalescence, Astrophys. J. 851, L35 (2017), arXiv: 1711.05578 [astroph.HE]
|
| 5 |
B. P. Abbott, et al. (LIGO Scientific Collaboration, Virgo Collaboration), GW170814: A three-detector observation of gravitational waves from a binary black hole coalescence, Phys. Rev. Lett. 119, 141101 (2017), arXiv: 1709.09660 [gr-qc]
|
| 6 |
B. P. Abbott, et al. (LIGO Scientific Collaboration, Virgo Collaboration), Binary black hole mergers in the first advanced LIGO observing run, Phys. Rev. X 6, 041015 (2016) [erratum: Phys. Rev. X 8, 039903 (2018)], arXiv: 1606.04856 [gr-qc]
|
| 7 |
B. P. Abbott, et al. (LIGO Scientific Collaboration, Virgo Collaboration), GWTC-1: A gravitational-wave transient catalog of compact binary mergers observed by LIGO and Virgo during the first and second observing runs, arXiv: 1811.12907 [astro-ph.HE] (2018)
|
| 8 |
B. P. Abbott, et al. (LIGO Scientific Collaboration, Virgo Collaboration), GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral, Phys. Rev. Lett. 119, 161101 (2017), arXiv: 1710.05832 [gr-qc]
|
| 9 |
G. González, A. Viceré, and L. Wen, Gravitational wave astronomy, Front. Phys. 8(6), 771 (2013)
https://doi.org/10.1007/s11467-013-0329-5
|
| 10 |
B. Zhang, The delay time of gravitational wave – gammaray burst associations, Front. Phys. 14, 64402 (2019), arXiv: 1905.00781 [astro-ph.HE]
https://doi.org/10.1007/s11467-019-0913-4
|
| 11 |
B. P. Abbott (LIGO Scientific Collaboration, Virgo Collaboration, KAGRA Collaboration), Prospects for observing and localizing gravitational-wave transients with Advanced LIGO, Advanced Virgo and KAGRA, Living Rev. Relativ. 21(1), 3 (2018), arXiv :1304.0670 [gr-qc]
|
| 12 |
R. Zhang, P. Isola, and A. A. Efros, Colorful image colorization, arXiv: 1603.08511 (2016)
https://doi.org/10.1007/978-3-319-46487-9_40
|
| 13 |
A. Karpathy, A. Joulin, and F. F. Li, Deep fragment embeddings for bidirectional image sentence mapping, arXiv: 1406.5679 (2014)
|
| 14 |
A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, Commun. ACM 60(6), 84 (2017)
https://doi.org/10.1145/3065386
|
| 15 |
I. Kononenko, Machine learning for medical diagnosis: History, state of the art and perspective, Artif. Intell. Med. 23(1), 89 (2001)
https://doi.org/10.1016/S0933-3657(01)00077-X
|
| 16 |
T. Gebhard, N. Kilbertus, G. Parascandolo, I. Harry, and B. Schölkopf, Convwave: Searching for gravitational waves with fully convolutional neural nets, in: Workshop on Deep Learning for Physical Sciences (DLPS) at the 31st Conference on Neural Information Processing Systems (2017)
|
| 17 |
D. George and E. A. Huerta, Deep neural networks to enable real-time multimessenger astrophysics, Phys. Rev. D 97, 044039 (2018), arXiv: 1701.00008 [astro-ph.IM]
https://doi.org/10.1103/PhysRevD.97.044039
|
| 18 |
D. George, H. Y. Shen, and E. A. Huerta, Deep transfer learning: A new deep learning glitch classification method for advanced LIGO, Phys. Rev. D 97, 101501(R) (2018), arXiv: 1706.07446 [gr-qc]
https://doi.org/10.1103/PhysRevD.97.101501
|
| 19 |
D. George and E. A. Huerta, Deep learning for real-time gravitational wave detection and parameter estimation: Results with advanced LIGO data, Phys. Lett. B 778, 64 (2018), arXiv: 1711.03121 [gr-qc]
https://doi.org/10.1016/j.physletb.2017.12.053
|
| 20 |
D. George, H. Y. Shen, and E. A. Huerta, Glitch classification and clustering for LIGO with deep transfer learning, Phys. Rev. D 97, 101501 (2018), arXiv: 1711.07468 [astroph.IM]
https://doi.org/10.1103/PhysRevD.97.101501
|
| 21 |
D. George and E. A. Huerta, Deep learning for real-time gravitational wave detection and parameter estimation with LIGO data, in: NiPS Summer School 2017 Gubbio, Perugia, Italy, June 30–July 3, 2017 (2017), arXiv: 1711.07966 [gr-qc]
|
| 22 |
H. Y. Shen, D. George, E. A. Huerta, and Z. Z. Zhao, Denoising gravitational waves using deep learning with recurrent denoising autoencoders, arXiv: 1711.09919 [grqc] (2017)
|
| 23 |
H. Gabbard, M. Williams, F. Hayes, and C. Messenger, Matching matched filtering with deep networks for gravitational-wave astronomy, Phys. Rev. Lett. 120, 141103 (2018), arXiv: 1712.06041 [astro-ph.IM]
https://doi.org/10.1103/PhysRevLett.120.141103
|
| 24 |
X. R. Li, W. L. Yu, and X. L. Fan, A method of detecting gravitational wave based on time-frequency analysis and convolutional neural networks, arXiv: 1712.00356 [astroph.IM] (2017)
|
| 25 |
X. L. Fan, J. Li, X. Li, Y. H. Zhong, and J. W. Cao, Applying deep neural networks to the detection and space parameter estimation of compact binary coalescence with a network of gravitational wave detectors, Sci. China Phys. Mech. Astron. 62, 969512 (2019), arXiv: 1811.01380 [astro-ph.IM]
https://doi.org/10.1007/s11433-018-9321-7
|
| 26 |
Z. J. Cao, H. Wang, and J. Y. Zhu, Initial study on the application of deep learning to the gravitational wave data analysis, J. Henan Norm. Univ. (Nat. Sci. Ed.) 46, 26 (2018)
|
| 27 |
T. D. Gebhard, N. Kilbertus, I. Harry, and B. Schölkopf, Convolutional neural networks: A magic bullet for gravitational-wave detection? arXiv: 1904.08693 [astroph.IM] (2019)
https://doi.org/10.1103/PhysRevD.100.063015
|
| 28 |
https://github.com/mj-will/intro2ml/blob/master/
|
| 29 |
R. Biswas, L. Blackburn, J. Cao, R. Essick, K. A. Hodge, E. Katsavounidis, K. Kim, Y. M. Kim, E. O. Le Bigot, C. H. Lee, J. J. Oh, S. H. Oh, E. J. Son, Y. Tao, R. Vaulin, and X. Wang, Application of machine learning algorithms to the study of noise artifacts in gravitationalwave data, Phys. Rev. D 88(6), 062003 (2013), arXiv: 1303.6984 [astro-ph.IM]
https://doi.org/10.1103/PhysRevD.88.062003
|
| 30 |
D. George, H. Y. Shen, and E. A. Huerta, Glitch classification and clustering for LIGO with deep transfer learning, in: NiPS Summer School 2017 Gubbio, Perugia, Italy, June 30-July 3, 2017 (2017), arXiv: 1711.07468 [astro-ph.IM]
|
| 31 |
M. Cavaglia, K. Staats, and T. Gill, Finding the origin of noise transients in LIGO data with machine learning, Commun. Comput. Phys. 25, 963 (2019), arXiv: 1812.05225 [physics.data-an]
https://doi.org/10.4208/cicp.OA-2018-0092
|
| 32 |
S. B. Coughlin, et al., Classifying the unknown: discovering novel gravitational-wave detector glitches using similarity learning, Phys. Rev. D 99, 082002 (2019), arXiv: 1903.04058 [astroph.IM]
https://doi.org/10.1103/PhysRevD.99.082002
|
| 33 |
M. Llorens-Monteagudo, T. F. Alejandro, J. Font, and A. Marquina, Classification of gravitational-wave glitches via dictionary learning, Class. Quant. Grav. 36, 075005 (2019), arXiv: 1811.03867 [astro-ph.IM]
https://doi.org/10.1088/1361-6382/ab0657
|
| 34 |
B. P. Abbott, et al. (LIGO Scientific Collaboration, Virgo Collaboration, KAGRA Collaboration), Prospects for observing and localizing gravitational-wave transients with advanced LIGO, Advanced Virgo and KAGRA, Living Rev. Rel. 21, 3 (2018), arXiv: 1304.0670 [gr-qc]
|
| 35 |
S. Husa, S. Khan, M. Hannam, M. Prrer, F. Ohme, X. J. Forteza, and A. Boh, Frequency-domain gravitational waves from nonprecessing black-hole binaries (I): New numerical waveforms and anatomy of the signal, Phys. Rev. D 93, 044006 (2016), arXiv: 1508.07250 [gr-qc]
https://doi.org/10.1103/PhysRevD.93.044006
|
| 36 |
S. Khan, S. Husa, M. Hannam, F. Ohme, M. Prrer, X. J. Forteza, and A. Boh, Frequency-domain gravitational waves from nonprecessing black-hole binaries (II): A phenomenological model for the advanced detector era, Phys. Rev. D 93, 044007 (2016), arXiv: 1508.07253 [gr-qc]
https://doi.org/10.1103/PhysRevD.93.044007
|
| 37 |
LIGO Scientific Collaboration, LIGO Algorithm Library- LALSuite, free software (GPL) (2018)
|
| 38 |
G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning: With Applications in R, Springer Texts in Statistics, Springer New York, 2013
https://doi.org/10.1007/978-1-4614-7138-7
|
| 39 |
B. D. Ripley and N. L. Hjort, Pattern Recognition and Neural Networks, 1st Ed., Cambridge University Press, New York, NY, USA, 1995
|
| 40 |
M. D. Zeiler and R. Fergus, Visualizing and understanding convolutional networks, arXiv: 1311.2901 (2013)
|
| 41 |
T. Dozat, Incorporating nesterov momentum into Adam (2015)
|
| 42 |
F. Chollet, et al., Keras, (2015)
|
| 43 |
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting, J. Mach. Learn. Res. 15, 1929 (2014)
|
| 44 |
J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. A. Riedmiller, Striving for simplicity: The all convolutional net, arXiv: 1412.6806 (2014)
|
| 45 |
T. Fawcett, An introduction to ROC analysis, Pattern Recognit. Lett. 27(8), 861 (2006)
https://doi.org/10.1016/j.patrec.2005.10.010
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