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Data mining and machine learning improve gravitational-wave detector sensitivity

Vajente, Gabriele (2022) Data mining and machine learning improve gravitational-wave detector sensitivity. Physical Review D, 105 (10). Art. No. 102005. ISSN 2470-0010. doi:10.1103/physrevd.105.102005.

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Application of data mining and machine learning techniques can significantly improve the sensitivity of current interferometric gravitational-wave detectors. Such instruments are complex multi-input single-output systems, with close-to-linear dynamics and hundreds of active feedback control loops. We show how the application of brute-force data-mining techniques allows us to discover correlations between auxiliary monitoring channels and the main gravitational-wave output channel. We also discuss the result of the application of a parametric and time-domain noise subtraction algorithm, that allows a significant improvement of the detector sensitivity at frequencies below 30 Hz.

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Vajente, Gabriele0000-0002-7656-6882
Additional Information:© 2022 American Physical Society. (Received 8 March 2022; accepted 10 May 2022; published 20 May 2022) This material is based upon work supported by NSF’s LIGO Laboratory which is a major facility fully funded by the National Science Foundation. The authors gratefully acknowledge the support of the United States National Science Foundation (NSF) for the construction and operation of the LIGO Laboratory and Advanced LIGO as well as the Science and Technology Facilities Council (STFC) of the United Kingdom, and the Max-Planck-Society (MPS) for support of the construction of Advanced LIGO. Additional support for Advanced LIGO was provided by the Australian Research Council. 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. Advanced LIGO was built under Grant No. PHY-0823459. The authors are grateful for computational resources provided by the LIGO Laboratory and supported by National Science Foundation Grants No. PHY-0757058 and No. PHY-0823459. This work carries LIGO Document number P2100472. We would like to thank all of the essential workers who put their health at risk during the COVID-19 pandemic, without whom we would not have been able to complete this work.
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Science and Technology Facilities Council (STFC)UNSPECIFIED
Australian Research CouncilUNSPECIFIED
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LIGO DocumentP2100472
Issue or Number:10
Record Number:CaltechAUTHORS:20220715-332037000
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:115626
Deposited By: George Porter
Deposited On:18 Jul 2022 17:21
Last Modified:18 Jul 2022 17:21

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