Sparsity-based recovery of Galactic-binary gravitational waves

Blelly, Aurore; Moutarde, H.; Bobin, J.

doi:10.1103/physrevd.102.104053

Cited by 7 publications

(17 citation statements)

References 17 publications

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“…As we emphasized in Ref. [16], being nearly mono-frequency the GBs waveform admits a naturally sparse distribution in the harmonic domain (Fourier basis). In other words, it can be approximated accurately with a small number of Fourier components.…”

Section: Sparse Signal Modelling and Recoverymentioning

confidence: 99%

“…In Ref. [16] we specifically focused on the recovery of GB signals from noisy GW data. In this context, we developed a sparsity-enforcing denoising algorithm derived from extensively used methods in the signal and image processing community.…”

Section: Sparse Signal Modelling and Recoverymentioning

confidence: 99%

“…At the same time, the noise is assumed to be Gaussian in the same domain, with a known power spectral density. These assumptions allowed us to develop a method [16] that can separate the total signal coming from GBs (as the sum of all signals coming from GBs) from the noise. The gist of such a method amounts to estimating the signal by looking for it as the minimizer of the following cost function:…”

Section: Sparse Signal Modelling and Recoverymentioning

confidence: 99%

“…Contributions : In Ref. [16], we introduced a novel, general, non-parametric framework for the detection and recovery of GBs. Built upon a sparsity-based modelling of the GBs, the proposed approach allows to account for the particular structures of TDI data as well as specificities of galactic binaries' waveform.…”

Section: Introduction 1general Contextmentioning

confidence: 99%

“…In this article, we therefore propose an innovative method that combines the nonparametric GBs recovery method we introduced in Ref. [16] and sparse data inpainting to mitigate the impact of data gaps on LISA science. Elaborating on our prior knowledge of the ungapped noise distribution, we estimate not only the missing signal but also the missing noise.…”

Section: Introduction 1general Contextmentioning

confidence: 99%

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Sparse data inpainting for the recovery of Galactic-binary gravitational wave signals from gapped data

Blelly,

Bobin,

Moutarde

2021

Preprint

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The forthcoming space-based gravitational wave observatory LISA will open a new window for the measurement of galactic binaries, which will deliver unprecedented information about these systems. However, the detection of galactic binary gravitational wave signals is challenged by the presence of gaps in the data. Whether being planned or not, gapped data dramatically reduce our ability to detect faint signals and the risk of misdetection soars. Inspired by advances in signal processing, we introduce a non-parametric inpainting algorithm based on the sparse representation of the galactic binary signal in the Fourier domain. In contrast to traditional inpainting approaches, noise statistics are known theoretically on ungapped measurements only. This calls for the joint recovery of both the ungapped noise and the galactic binary signal. We thoroughly show that sparse inpainting yields an accurate estimation of the gravitational imprint of the galactic binaries. Additionally, we highlight that the proposed algorithm produces a statistically consistent ungapped noise estimate. We further evaluate the performances of the proposed inpainting methods to recover the gravitational wave signal on a simple example involving verification galactic binaries recently proposed in LISA data challenges.

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Section: Sparse Signal Modelling and Recoverymentioning

confidence: 99%

Section: Sparse Signal Modelling and Recoverymentioning

confidence: 99%

Section: Sparse Signal Modelling and Recoverymentioning

confidence: 99%

Section: Introduction 1general Contextmentioning

confidence: 99%

Section: Introduction 1general Contextmentioning

confidence: 99%

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