Reconstruction of Black Hole Ringdown Signals with Data Gaps using a Deep-Learning Framework
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915592253997056 |
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| author | Lai, Jing-Qi Jiao, Jia-Geng Shao, Cai-Ying Shi, Jun-Xi Tian, Yu |
| author_facet | Lai, Jing-Qi Jiao, Jia-Geng Shao, Cai-Ying Shi, Jun-Xi Tian, Yu |
| contents | We introduce DenoiseGapFiller (DGF), a deep-learning framework specifically designed to reconstruct gravitational-wave ringdown signals corrupted by data gaps and instrumental noise. DGF employs a dual-branch encoder-decoder architecture, which is fused via mixing layers and Transformer-style blocks. Trained end-to-end on synthetic ringdown waveforms with gaps up to 20% of the segment length, DGF can achieve a mean waveform mismatch of 0.002. The residual amplitudes of the Time-domain shrink by roughly an order of magnitude and the power spectral density in the 0.01-1 Hz band is suppressed by 1-2 orders of magnitude, restoring the peak of quasi-normal mode(QNM) in the time-frequency representation around 0.01-0.1 Hz. The ability of the model to faithfully reconstruct the original signals, which implies milder penalties in the detection evidence and tighter credible regions for parameter estimation, lay a foundation for the following scientific work. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_00834 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reconstruction of Black Hole Ringdown Signals with Data Gaps using a Deep-Learning Framework Lai, Jing-Qi Jiao, Jia-Geng Shao, Cai-Ying Shi, Jun-Xi Tian, Yu General Relativity and Quantum Cosmology Instrumentation and Methods for Astrophysics Data Analysis, Statistics and Probability We introduce DenoiseGapFiller (DGF), a deep-learning framework specifically designed to reconstruct gravitational-wave ringdown signals corrupted by data gaps and instrumental noise. DGF employs a dual-branch encoder-decoder architecture, which is fused via mixing layers and Transformer-style blocks. Trained end-to-end on synthetic ringdown waveforms with gaps up to 20% of the segment length, DGF can achieve a mean waveform mismatch of 0.002. The residual amplitudes of the Time-domain shrink by roughly an order of magnitude and the power spectral density in the 0.01-1 Hz band is suppressed by 1-2 orders of magnitude, restoring the peak of quasi-normal mode(QNM) in the time-frequency representation around 0.01-0.1 Hz. The ability of the model to faithfully reconstruct the original signals, which implies milder penalties in the detection evidence and tighter credible regions for parameter estimation, lay a foundation for the following scientific work. |
| title | Reconstruction of Black Hole Ringdown Signals with Data Gaps using a Deep-Learning Framework |
| topic | General Relativity and Quantum Cosmology Instrumentation and Methods for Astrophysics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2511.00834 |