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Concurrent estimation of noise and compact-binary signal parameters in gravitational-wave data

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arxiv 2208.02291 v2 pith:2NWYULEB submitted 2022-08-03 gr-qc astro-ph.HE

classification gr-qcastro-ph.HE
keywords noiseparametersestimationuncertaintybinarycompactdensitydetector
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Gravitational-wave parameter estimation for compact binary signals typically relies on sequential estimation of the properties of the detector Gaussian noise and of the binary parameters. This procedure assumes that the noise variance, expressed through its power spectral density, is perfectly known in advance. We assess the impact of this approximation on the estimated parameters by means of an analysis that simultaneously estimates the noise and compact binary parameters, thus allowing us to marginalize over uncertainty in the noise properties. We compare the traditional sequential estimation method and the new full marginalization method using events from the GWTC-3 catalog. We find that the recovered signals and inferred parameters agree to within their statistical measurement uncertainty. At current detector sensitivities, uncertainty about the noise power spectral density is a subdominant effect compared to other sources of uncertainty.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Compressive Meta-Learning

    cs.LG 2025-08 unverdicted novelty 6.0 of 10

    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.

  2. Joint inference for gravitational wave signals and glitches using a data-informed glitch model

    gr-qc 2025-05 conditional novelty 6.0 of 10

    A normalising flow trained on real Blip glitches provides a data-informed prior that, used jointly with the signal model in Bilby, removes glitches and reduces bias in gravitational-wave parameter estimates.

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