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Foreground signals minimally affect inference of high-mass binary black holes in next generation gravitational-wave detectors
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Next-generation gravitational-wave observatories are expected to detect over a thousand compact binary coalescence signals daily, with some lasting from minutes to hours. Consequently, multiple signals will overlap in the time-frequency plane, generating a "foreground noise" that predominantly affects the low-frequency range, where binary neutron star inspiral evolution is gradual. This study investigates the impact of such foreground noise on parameter estimation for short-duration binary black hole signals, particularly those with high detector-frame masses and/or located at large redshifts. Our results show a reduction in detection sensitivity by approximately 25\% when the noise power spectrum deviates by up to 50\% from Gaussian noise due to foreground contamination. Despite this, using standard parameter estimation techniques without subtracting overlapping signals, we find that foreground noise has minimal impact, primarily affecting precision. These findings suggest that even in the presence of substantial foreground noise, global-fit techniques, and/or signal subtraction will not be necessary, as accurate recovery of system parameters is achievable with minimal loss in precision.
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Cited by 1 Pith paper
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Hierarchical Subtraction with Neural Density Estimators as a General Solution to Overlapping Gravitational Wave Signals
The paper introduces an iterative, ensemble-based hierarchical subtraction scheme powered by neural density estimators that recovers overlapping gravitational wave signals accurately and fast.
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