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Journal Article

Noisy neighbours: inference biases from overlapping gravitational-wave signals

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Antonelli,  Andrea
Astrophysical and Cosmological Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society;

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Burke,  Ollie
Astrophysical and Cosmological Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society;

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Gair,  Jonathan
Astrophysical and Cosmological Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society;

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2104.01897.pdf
(Preprint), 2MB

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Citation

Antonelli, A., Burke, O., & Gair, J. (2021). Noisy neighbours: inference biases from overlapping gravitational-wave signals. Monthly Notices of the Royal Astronomical Society, 507(4), 5069-5086. doi:10.1093/mnras/stab2358.


Cite as: http://hdl.handle.net/21.11116/0000-0008-464A-7
Abstract
Understanding and dealing with inference biases in gravitational-wave (GW) parameter estimation when a plethora of signals are present in the data is one of the key challenges for the analysis of data from future GW detectors. Working within the linear signal approximation, we describe generic metrics to predict inference biases on GW source parameters in the presence of confusion noise from unfitted foregrounds, from overlapping signals that coalesce close in time to one another, and from residuals of other signals that have been incorrectly fitted out. We illustrate the formalism with simplified, yet realistic, scenarios appropriate to third-generation ground-based (Einstein Telescope) and space-based (LISA) detectors, and demonstrate its validity against Monte-Carlo simulations. We find it to be a reliable tool to cheaply predict the extent and direction of the biases. Finally, we show how this formalism can be used to correct for biases that arise in the sequential characterisation of multiple sources in a single data set, improving the accuracy of the global-fit without the need for expensive joint-fitting of the sources.