Machine-learning nonstationary noise out of gravitational-wave detectors

Signal extraction out of background noise is a common challenge in high-precision physics experiments, where the measurement output is often a continuous data stream. To improve the signal-to-noise ratio of the detection, witness sensors are often used to independently measure background noises and...

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Main Authors: Vajente, G. (Author), Huang, Yiwen (Author), Isi Banales, Maximiliano S (Author), Driggers, J. C (Author), Kissel, J. S (Author), Szczepańczyk, M. J (Author), Vitale, Salvatore (Author)
Other Authors: MIT Kavli Institute for Astrophysics and Space Research (Contributor)
Format: Article
Language:English
Published: American Physical Society, 2020-03-27T14:27:07Z.
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Online Access:Get fulltext
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042 |a dc 
100 1 0 |a Vajente, G.  |e author 
100 1 0 |a MIT Kavli Institute for Astrophysics and Space Research  |e contributor 
700 1 0 |a Huang, Yiwen  |e author 
700 1 0 |a Isi Banales, Maximiliano S  |e author 
700 1 0 |a Driggers, J. C.  |e author 
700 1 0 |a Kissel, J. S.  |e author 
700 1 0 |a Szczepańczyk, M. J.  |e author 
700 1 0 |a Vitale, Salvatore  |e author 
245 0 0 |a Machine-learning nonstationary noise out of gravitational-wave detectors 
260 |b American Physical Society,   |c 2020-03-27T14:27:07Z. 
856 |z Get fulltext  |u https://hdl.handle.net/1721.1/124384 
520 |a Signal extraction out of background noise is a common challenge in high-precision physics experiments, where the measurement output is often a continuous data stream. To improve the signal-to-noise ratio of the detection, witness sensors are often used to independently measure background noises and subtract them from the main signal. If the noise coupling is linear and stationary, optimal techniques already exist and are routinely implemented in many experiments. However, when the noise coupling is nonstationary, linear techniques often fail or are suboptimal. Inspired by the properties of the background noise in gravitational wave detectors, this work develops a novel algorithm to efficiently characterize and remove nonstationary noise couplings, provided there exist witnesses of the noise source and of the modulation. In this work, the algorithm is described in its most general formulation, and its efficiency is demonstrated with examples from the data of the Advanced LIGO gravitational-wave observatory, where we could obtain an improvement of the detector gravitational-wave reach without introducing any bias on the source parameter estimation. Keywords: Gravitational wave detection; meteorology; gravitational waves 
546 |a en 
655 7 |a Article 
773 |t Physical Review D