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

TL;DR

This paper introduces a Kalman-smoother based data imputation method to effectively address data gaps in spaceborne gravitational wave detectors, reducing biases and computational costs in parameter estimation.

Contribution

It presents a novel, efficient data imputation strategy using Kalman filter and smoother specifically designed for space-based gravitational wave data with gaps.

Findings

Reduces bias in parameter estimation caused by data gaps.

Lower computational cost compared to existing data augmentation methods.

Effective in mitigating spectral leakage due to data gaps.

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.