Inferring the spins of merging black holes in the presence of data-quality issues

Gravitational waves from black hole binary mergers carry information about the component spins, but inference is sensitive to analysis assumptions, which may be broken by terrestrial noise transients known as glitches. Using a variety of simulated glitches and gravitational wave signals, we study the conditions under which glitches can bias spin measurements. We confirm the theoretical expectation that inference and subtraction of glitches invariably leaves behind residual power due to statistical uncertainty, no matter the strength (signal-to-noise ratio, SNR) of the original glitch. Next we show that low-SNR glitches—including those below the threshold for flagging data-quality issues—can still significantly bias spin inference. Such biases occur for a range of glitch morphologies, even in cases where glitches and signals are not precisely aligned in phase. Furthermore, we find that residuals of glitch subtraction can result in biases as well. Our results suggest that joint inference of the glitch and gravitational wave parameters, with appropriate models and priors, is required to address these uncertainties inherent in glitch mitigation via subtraction.