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A Kalman-smoother based data imputation strategy to data gaps in spaceborne gravitational wave detectors

Tingyang Shen, He Wang2, Jibo He

  • 2 Corresponding author

PreprintarXiv:2507.024582025

First public:

  • Detection & denoising
  • Parameter inference

Research summary

Gaps in long gravitational-wave observations can remove information and introduce spectral leakage. This preprint develops a Kalman filter and smoother approach to reconstruct missing data for space-based detectors. Simulated massive-black-hole-binary examples examine how the treatment affects parameter-estimation bias and computational cost relative to other gap-handling strategies.

Extended notes

The proposed imputation strategy addresses gaps in long-duration observations. Comparisons with windowing and data-augmentation approaches examine the tradeoff between inference bias and computational effort.

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