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Gravitational-wave astronomy with an uncertain noise power spectral density

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arxiv 2006.05292 v2 pith:LZSYRPZN submitted 2020-06-09 astro-ph.IM astro-ph.HEgr-qc

classification astro-ph.IMastro-ph.HEgr-qc
keywords gravitational-wavenoisedatauncertaintydensityestimationmethodspower
verification ladder T0 review T1 audit T2 compute T3 formal
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In order to extract information about the properties of compact binaries, we must estimate the noise power spectral density of gravitational-wave data, which depends on the properties of the gravitational-wave detector. In practice, it is not possible to know this perfectly, only to estimate it from the data. Multiple estimation methods are commonly used and each has a corresponding statistical uncertainty. However, this uncertainty is widely ignored when measuring the physical parameters describing compact binary coalescences, and the appropriate likelihoods which account for the uncertainty are not well known. In order to perform increasingly precise astrophysical inference and model selection, it will be essential to account for this uncertainty. In this work, we derive the correct likelihood for one of the most widely used estimation methods in gravitational-wave transient analysis, the median average. We demonstrate that simulated Gaussian noise follows the predicted distributions. We then examine real gravitational-wave data at and around the time of GW151012, a relatively low-significance binary black hole merger event. We show that the data are well described by stationary-Gaussian noise and explore the impact of different noise power spectral density estimation methods on the astrophysical inferences we draw about GW151012.

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Cited by 2 Pith papers

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    Meta-learned neural encoders and decoders for compressive learning aim to make parameter estimation from compact database sketches faster and more accurate than randomized, data-independent compressive learning.

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    gr-qc 2025-06 conditional novelty 6.0 of 10

    A mock data challenge shows that a phase-coherent search for the binary black hole background can recover injected signal fractions in realistic noise, using new treatments of noise uncertainty, finite-duration effect...

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