Gravitational Wave Signal Denoising and Merger Time Prediction By Deep Neural Network
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929645936443392 |
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| author | Xu, Yuxiang Wang, He Du, Minghui Liang, Bo Xu, Peng |
| author_facet | Xu, Yuxiang Wang, He Du, Minghui Liang, Bo Xu, Peng |
| contents | The mergers of massive black hole binaries could generate rich electromagnetic emissions, which allow us to probe the environments surrounding these massive black holes and gain deeper insights into the high energy astrophysics. However, due to the short timescale of binary mergers, it is crucial to predict the time of the merger in advance to devise detailed observational plans. The overwhelming noise and the slow accumulation of signal-to-noise ratio in the inspiral phase make this task particularly challenging. To address this issue, we propose a novel deep neural denoising network in this study, capable of denoising a 30-day inspiral phase signal. Following the denoising process, we perform the detection and merger time prediction based on the denoised signals. Our results demonstrate that for a 30-day inspiral phase data with a signal-to-noise ratio between 10 and 50 occurring no more than 10 days before the merger, our absolute prediction error for the merger time is generally within 24 hours. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_08788 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Gravitational Wave Signal Denoising and Merger Time Prediction By Deep Neural Network Xu, Yuxiang Wang, He Du, Minghui Liang, Bo Xu, Peng General Relativity and Quantum Cosmology Astrophysics of Galaxies High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics The mergers of massive black hole binaries could generate rich electromagnetic emissions, which allow us to probe the environments surrounding these massive black holes and gain deeper insights into the high energy astrophysics. However, due to the short timescale of binary mergers, it is crucial to predict the time of the merger in advance to devise detailed observational plans. The overwhelming noise and the slow accumulation of signal-to-noise ratio in the inspiral phase make this task particularly challenging. To address this issue, we propose a novel deep neural denoising network in this study, capable of denoising a 30-day inspiral phase signal. Following the denoising process, we perform the detection and merger time prediction based on the denoised signals. Our results demonstrate that for a 30-day inspiral phase data with a signal-to-noise ratio between 10 and 50 occurring no more than 10 days before the merger, our absolute prediction error for the merger time is generally within 24 hours. |
| title | Gravitational Wave Signal Denoising and Merger Time Prediction By Deep Neural Network |
| topic | General Relativity and Quantum Cosmology Astrophysics of Galaxies High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2410.08788 |