Title: A Kalman-smoother based data imputation strategy to data gaps in spaceborne gravitational wave detectors Show Chinese title

Title: 基于卡尔曼平滑器的数据填补策略用于星载引力波探测器中的数据缺失

Abstract: Massive black hole binaries (MBHBs) and other sources within the frequency band of spaceborne gravitational wave observatories like the Laser Interferometer Space Antenna (LISA), Taiji and Tianqin pose unique challenges, as gaps and glitches during the years-long observation lead to both loss of information and spectral leakage. We propose a novel data imputation strategy based on Kalman filter and smoother to mitigate gap-induced biases in parameter estimation. Applied to a scenario where traditional windowing and smoothing technique introduce significant biases, our method mitigates the biases and demonstrates lower computational cost compared to existing data augmentation techniques such as noise inpainting. This framework presents a new gap treatment approach that balances robustness and efficiency for space-based gravitational wave data analysis. Show Chinese abstract

Abstract: 超大质量黑洞双星（MBHBs）和其他位于空间引力波观测仪如激光干涉空间天线（LISA）、太极和天琴等的频段内的源，带来了独特的挑战，因为在多年的观测中出现的间隙和故障会导致信息丢失和谱泄漏。我们提出了一种基于卡尔曼滤波器和平滑器的新颖数据填补策略，以减轻参数估计中的间隙引起的偏差。应用于传统加窗和平滑技术引入显著偏差的情景中，我们的方法减轻了偏差，并且与现有的数据增强技术（如噪声填充）相比计算成本更低。该框架提出了一种新的间隙处理方法，为基于空间的引力波数据分析提供了鲁棒性和效率之间的平衡。