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Published February 15, 2020 | Published
Journal Article Open

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 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.

Additional Information

© 2020 American Physical Society. (Received 20 November 2019; accepted 10 January 2020; published 18 February 2020) The authors thank C. J. Haster for help generating the simulated sources described in Sec. IV.3. LIGO was constructed by the California Institute of Technology and Massachusetts Institute of Technology with funding from the National Science Foundation and operates under cooperative agreement No. PHY-1764464. M. I. is supported by NASA through the NASA Hubble Fellowship Grant No. HST-HF2-51410.001-A awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., for NASA, under Contract No. NAS5-26555. M. J. S. is supported by the National Science Foundation Grant No. PHY-1806165. The authors are grateful for computational resources provided by the LIGO Laboratory and supported by the National Science Foundation Grants No. PHY-0757058 and No. PHY-0823459. This paper has LIGO document No. P1900335.

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August 19, 2023
October 19, 2023