Gravitational waves are 'ripples' in space-time caused by some of the most violent and energetic processes in the Universe [6]. These cosmic ripples would travel at the speed of light, carrying with them information about their origins, as well as clues to the nature of gravity itself. Albert Einstein predicted the existence of gravitational waves in 1916 in his general theory of relativity.
The strongest gravitational waves are produced by cataclysmic events such as colliding black holes, supernovae, and colliding neutron stars. Other waves are predicted to be caused by the rotation of neutron stars that are not perfect spheres, and possibly even the remnants of gravitational radiation created by the Big Bang.
The animation below illustrates how gravitational waves are emitted by two neutron stars as they orbit each other and then coalesce (credit: NASA/Goddard Space Flight Center). Note that gravitational waves themselves are invisible. They are made visible here to illustrate their propagation away from the source.
video.mp4
To learn more about Gravitation Waves, you can visit these tutorials hosted on the GWOSC website or complete the GWOSC Open Workshop.
According to Krastev [1], the groundbreaking discoveries of gravitational waves from binary black-hole mergers and coalescing neutron stars have started a new era of Multi-Messenger Astrophysics.
These discoveries were made possible by the Advanced Laser Interferometer Gravitational Wave Observatory (LIGO) and Virgo collaborations. As gravitational-wave detectors increase their sensitivity many more observations, including BBH, binary neutron star (BNS) and black hole - neutron star (BHNS) signals are likely to be detected more frequently.
The computational cost of the low-latency GW searches based on implementations of matched-filtering is presently such that their extension to the full manifold is infeasible. Most importantly, these surveys may miss important GW transients where a rapid follow-up is critical for successful observation of their electromagnetic counterparts due to their fast decay rate. They need to be identified and localized within several hours after the compact binary merger and promptly observed in the entire electromagnetic spectrum.
Therefore, based on the above considerations, the need arises for new methods to overcome the limitations and computational challenges of existing GW detection algorithms. Deep-learning methods are able to perform analysis rapidly since the computationally intensive part of the algorithm is done during the training stage before the actual inference, which could make them multitudes faster than the conventional match-filtering techniques.
In this project we use various deep-learning techniques to rapidly identify transient gravitational-wave signals from binary neutron star and black-hole mergers in a noisy time series representative of typical gravitational-wave detector data.
- Python - 3.6.9
- GWpy - 2.1.3
- PyCBC - 1.18.3
- Tensorflow - 2.3.0
- Keras - 2.3.1
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Chauhan, Y., 2020. Deep Learning Techniques to Make Gravitational Wave Detections from Weak Time-series Data. arXiv preprint arXiv:2007.05889.
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Krastev, Plamen. (2019). Real-Time Detection of Gravitational Waves from Binary Neutron Stars using Artificial Neural Networks.
- Razzano, M. and Cuoco, E., 2018. Image-based deep learning for classification of noise transients in gravitational wave detectors. Classical and Quantum Gravity, 35(9), p.095016.
- Dodia, H., Tandel, H. and D'Mello, L., 2021. SpecGrav--Detection of Gravitational Waves using Deep Learning. arXiv preprint arXiv:2107.03607.
- Ormiston, R., Nguyen, T., Coughlin, M., Adhikari, R.X. and Katsavounidis, E., 2020. Noise reduction in gravitational-wave data via deep learning. Physical Review Research, 2(3), p.033066.
- https://www.ligo.caltech.edu/page/what-are-gw
- https://www.gw-openscience.org/
- Tang, W., Long, G., Liu, L., Zhou, T., Jiang, J. and Blumenstein, M., 2020. Rethinking 1d-cnn for time series classification: A stronger baseline. arXiv preprint arXiv:2002.10061.