Title: Reconstruction of Black Hole Ringdown Signals with Data Gaps using a Deep-Learning Framework Show Chinese title

Title: 使用深度学习框架重建存在数据缺口的黑洞旋进信号

Abstract: 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. Show Chinese abstract

Abstract: 我们引入了去噪填补器（DenoiseGapFiller，DGF），这是一个专门设计用于重建被数据缺口和仪器噪声破坏的引力波旋尾信号的深度学习框架。 DGF采用双分支编码器-解码器架构，通过混合层和Transformer风格的块进行融合。 在包含最多段长20%缺口的合成旋尾波形上端到端训练，DGF可以达到平均波形不匹配度为0.002。 时域中的残差振幅大约减少了一个数量级，0.01-1 Hz频段的功率谱密度被抑制了1-2个数量级，在0.01-0.1 Hz附近的时频表示中恢复了准正则模（QNM）的峰值。 该模型能够忠实重建原始信号的能力，意味着在检测证据中惩罚更小，并且参数估计的可信区域更紧密，为后续的科学研究奠定了基础。