Abstract
Since the Gravitational Waves’ initial direct detection, a veil of mystery from the Universe has been lifted, ushering a new era of intriguing physics, as-tronomy, and astrophysics research. Unfortunately, since then, not much progress has been reported, because so far all of the detected Gravitational Waves fell only into the Binary bursting wave type (B-GWs), which are cre-ated via spinning binary compact objects such as black holes. Nowadays, as-tronomy scientists seek to detect a new type of gravitational waves called: Continuous Gravitational Waves (C-GWs). Unlike the complicated burst na-ture of B-GWs, C-GWs have elegant and much simpler form, being able to provide higher quality of information for the Universe exploration. Never-theless, C-GWs are much weaker comparing to the B-GWs, which makes them considerably harder to be detected. For this task, we propose a novel Deep-Learning-based methodology, being sensitive enough for detecting and visualizing C-GWs, based on Short-Time-Fourier data provided by LIGO. Based on extensive experimental simulations, our approach significantly outperformed the state-of-the-art approaches, for every applied experimental configuration, revealing the efficiency of the proposed methodology. Our expectation is that this work can potentially assist scientists to improve their detection sensitivity, leading to new Astrophysical discoveries, via the incor-poration of Data-Mining and Deep-Learning sciences.
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Notes
- 1.
For visualization purposes, we averaged the multi channels spectrograms to 1-channel.
- 2.
The datasets used in our research, can be found in https://www.kaggle.com/datasets/emmanuelpintelas/gw-datasets.
References
Abbott, B.P., et al.: GW170817: observation of gravitational waves from a binary neutron star inspiral. Phys. Rev. Letters 119(16), 161101 (2017)
Abbott, B.P., et al.: Observation of gravitational waves from a binary black hole merger. Phys. Rev. Lett. 116(6), 061102 (2016)
Abbott, B.P., Abbott, R., Abbott, T., Abraham, S., Acernese, F., Ackley, K., et al.: All-sky search for continuous gravitational waves from isolated neutron stars using advanced LIGO O2 data. Phys. Rev. D 100(2), 102008 (2019)
Abbott, B.P., et al.: Exploring the sensitivity of next generation gravitational wave detectors. Classical and Quantum Gravity 34(4), 044001 (2017)
Abbott, B., et al.: LIGO: the laser interferometer gravitational-wave observatory. Reports Progress Phys. 72(7), 076901 (2009)
Abbott, R., et al.: Searches for gravitational waves from known pulsars at two harmonics in the second and third LIGO-Virgo observing runs. Astrophys J 935(1), 1 (2022)
Byrne, C.L.: Signal Processing: a mathematical approach. CRC Press (2014)
Caprini, C., Figueroa, D.G.: Cosmological backgrounds of gravitational waves. Classical Quant. Gravity 35(16), 163001 (2018)
George, D., Huerta, E.A.: Deep learning for real-time gravitational wave detection and parameter estimation: results with advanced LIGO data. Phys. Lett. B 778, 64–70 (2018)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
Koonce, B., Koonce, B.: Efficientnet. Convolutional Neural Networks with Swift for Tensorflow: Image Recognition and Dataset Categorization, pp. 109–123 (2021)
Królak, A., Patil, M.: The first detection of gravitational waves. Universe 3(3), 59 (2017)
Livieris, I.E., Pintelas, E., Kiriakidou, N., Stavroyiannis, S.: An advanced deep learning model for short-term forecasting us natural gas price and movement. In: Artificial Intelligence Applications and Innovations, pp. 165–176. Springer (2020)
Livieris, I.E., Pintelas, E., Pintelas, P.: A CNN-LSTM model for gold price time-series forecasting. Neural Comput. Appl. 32, 17351–17360 (2020)
Livieris, I.E., Pintelas, P.: An adaptive nonmonotone active set-weight constrained-neural network training algorithm. Neurocomputing 360, 294–303 (2019)
Livieris, I.E., Stavroyiannis, S., Pintelas, E., Pintelas, P.: A novel validation framework to enhance deep learning models in time-series forecasting. Neural Comput. Appl. 32(23), 17149–17167 (2020). https://doi.org/10.1007/s00521-020-05169-y
Lu, L., Zheng, Y., Carneiro, G., Yang, L.: Deep learning and convolutional neural networks for medical image computing. Adv. Comput. Vis. Pattern Recognit. 10, 978–3 (2017)
Park, D.S., et al.: Specaugment: A simple data augmentation method for automatic speech recognition. arXiv preprint arXiv:1904.08779 (2019)
Pintelas, E., Liaskos, M., Livieris, I.E., Kotsiantis, S., Pintelas, P.: A novel explainable image classification framework: Case study on skin cancer and plant disease prediction. Neural Comput. Appl. 33(22), 15171–15189 (2021)
Pintelas, E., Livieris, I.E., Pintelas, P.: A grey-box ensemble model exploiting black-box accuracy and white-box intrinsic interpretability. Algorithms 13(1), 17 (2020)
Purwins, H., Li, B., Virtanen, T., Schlüter, J., Chang, S.Y., Sainath, T.: Deep learning for audio signal processing. IEEE J. Selected Topics Signal Process. 13(2), 206–219 (2019)
Raschka, S.: An overview of general performance metrics of binary classifier systems. arXiv preprint arXiv:1410.5330 (2014)
Riles, K.: Recent searches for continuous gravitational waves. Mod. Phys. Lett. A 32(39), 1730035 (2017)
Roscher, R., Bohn, B., Duarte, M.F., Garcke, J.: Explainable machine learning for scientific insights and discoveries. IEEE Access 8, 42200–42216 (2020)
Rothman, T.: The secret history of gravitational waves: contrary to popular belief, einstein was not the first to conceive of gravitational waves-but he was, eventually, the first to get the concept right. Am. Sci. 106(2), 96–104 (2018)
Schäfer, M.B., Ohme, F., Nitz, A.H.: Detection of gravitational-wave signals from binary neutron star mergers using machine learning. Physical Review D 102(6), 063015 (2020)
Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Véstias, M.P.: Convolutional neural network. In: Encyclopedia of Information Science and Technology, Fifth Edition, pp. 12–26. IGI Global (2021)
Wei, W., Huerta, E.: Deep learning for gravitational wave forecasting of neutron star mergers. Phys. Lett. B 816, 136185 (2021)
Zhang, H., et al.: Resnest: Split-attention networks. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2736–2746 (2022)
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Pintelas, E., Livieris, I.E., Pintelas, P. (2023). A Deep Learning-Based Methodology for Detecting and Visualizing Continuous Gravitational Waves. In: Maglogiannis, I., Iliadis, L., MacIntyre, J., Dominguez, M. (eds) Artificial Intelligence Applications and Innovations. AIAI 2023. IFIP Advances in Information and Communication Technology, vol 675. Springer, Cham. https://doi.org/10.1007/978-3-031-34111-3_1
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