Window and inpainting: dealing with data gaps for TianQin

Abstract

Space-borne gravitational wave detectors like TianQin might encounter data gaps due to factors like micro-meteoroid collisions or hardware failures. Such glitches will cause discontinuity in the data and have been observed in the LISA Pathfinder. The existence of such data gaps presents challenges to the data analysis for TianQin, especially for massive black hole binary mergers, since its signal-to-noise ratio (SNR) accumulates in a non-linear way, a gap near the merger could lead to significant loss of SNR. It could introduce bias in the estimate of noise properties, and furthermore the results of the parameter estimation. In this work, using simulated TianQin data with injected a massive black hole binary merger, we study the window function method, and for the first time, the inpainting method to cope with the data gap, and an iterative estimate scheme is designed to properly estimate the noise spectrum. We find that both methods can properly estimate noise and signal parameters. The easy-to-implement window function method can already perform well, except that it will sacrifice some SNR due to the adoption of the window. The inpainting method is slower, but it can minimize the impact of the data gap.