Dealing with data gaps for TianQin with massive black hole binary signal

TL;DR

This paper investigates methods to handle data gaps in TianQin gravitational wave data, demonstrating that both window function and inpainting techniques can effectively recover signals and noise properties, with trade-offs in complexity and SNR preservation.

Contribution

It introduces and compares the first application of inpainting and window function methods to mitigate data gaps in TianQin gravitational wave data analysis.

Findings

Both methods accurately estimate noise and signal parameters.

Window function method is simple but reduces SNR.

Inpainting method minimizes data gap impact but is slower.

Abstract

Space-borne gravitational wave detectors like TianQin might encounter data gaps due to factors like micrometeoroid collisions or hardware failures. Such events will cause discontinuity in the data, presenting challenges to the data analysis for TianQin, especially for massive black hole binary mergers. Since the signal-to-noise ratio (SNR) accumulates in a non-linear way, a gap near the merger could lead to a significant loss of SNR. It could introduce bias in the estimate of noise properties, and 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.