Binary Black Hole Parameter Estimation from Gravitational Waves with Deep Learning Methods
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-62495-7_6
Reference21 articles.
1. Bailes, M., et al.: Gravitational-wave physics and astronomy in the 2020s and 2030s. Nat. Rev. Phys. 3(5), 344–366 (2021)
2. Bohé, A., et al.: Improved effective-one-body model of spinning, nonprecessing binary black holes for the era of gravitational-wave astrophysics with advanced detectors. Phys. Rev. D 95, 044028 (2017). https://doi.org/10.1103/PhysRevD.95.044028
3. Corizzo, R., Ceci, M., Zdravevski, E., Japkowicz, N.: Scalable auto-encoders for gravitational waves detection from time series data. Expert Syst. Appl. 151, 113378 (2020). https://doi.org/10.1016/j.eswa.2020.113378
4. Fan, X., Li, J., Li, X., Zhong, Y., Cao, J.: 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(6) (2019). https://doi.org/10.1007/s11433-018-9321-7
5. Gabbard, H., Williams, M., Hayes, F., Messenger, C.: Matching matched filtering with deep networks for gravitational-wave astronomy. Phys. Rev. Lett. 120, 141103 (2018). https://doi.org/10.1103/PhysRevLett.120.141103
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