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New methods to assess and improve LIGO detector duty cycle

Biswas, A. and McIver, J. and Mahabal, A. (2020) New methods to assess and improve LIGO detector duty cycle. Classical and Quantum Gravity, 37 (17). Art. No. 175008. ISSN 0264-9381.

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A network of three or more gravitational wave detectors simultaneously taking data is required to generate a well-localized sky map for gravitational wave sources, such as GW170817. Local seismic disturbances often cause the LIGO and Virgo detectors to lose light resonance in one or more of their component optic cavities, and the affected detector is unable to take data until resonance is recovered. In this paper, we use machine learning techniques to gain insight into the predictive behavior of the LIGO detector optic cavities during the second LIGO–Virgo observing run. We identify a minimal set of optic cavity control signals and data features which capture interferometer behavior leading to a loss of light resonance, or lockloss. We use these channels to accurately distinguish between lockloss events and quiet interferometer operating times via both supervised and unsupervised machine learning methods. This analysis yields new insights into how components of the LIGO detectors contribute to lockloss events, which could inform detector commissioning efforts to mitigate the associated loss of uptime. Particularly, we find that the state of the component optical cavities is a better predictor of loss of lock than ground motion trends. We report prediction accuracies of 98% for times just prior to lock loss, and 90% for times up to 30 s prior to lockloss, which shows promise for this method to be applied in near-real time to trigger preventative detector state changes. This method can be extended to target other auxiliary subsystems or times of interest, such as transient noise or loss in detector sensitivity. Application of these techniques during the third LIGO–Virgo observing run and beyond would maximize the potential of the global detector network for multi-messenger astronomy with gravitational waves.

Item Type:Article
Related URLs:
URLURL TypeDescription Paper
Biswas, A.0000-0001-9609-7158
McIver, J.0000-0003-0316-1355
Mahabal, A.0000-0003-2242-0244
Additional Information:© 2020 IOP Publishing Ltd. Received 28 October 2019, revised 24 March 2020; Accepted for publication 3 April 2020; Published 3 August 2020. The authors thank Jameson Rollins for useful discussion on other relevant work ongoing within the LIGO Scientific Collaboration as well as developing and maintaining the code used to identify precise lockloss times we used for a labelled data set. We also thank Sheila Dwyer for guidance on auxiliary witnesses that would be fruitful to target for this study. The authors are grateful for computational resources provided by the LIGO Laboratory and supported by National Science Foundation Grants PHY-0757058 and PHY-0823459. AB would like to thank IIT Gandhinagar, the Caltech SURF program, and LIGO, Caltech for support during the study. AM acknowledges support from the NSF (1640818, AST-1815034). AM and JM also acknowledge support from IUSSTF (JC-001/2017). 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 PHY-0757058. Advanced LIGO was built under award PHY-0823459. This paper carries LIGO Document Number LIGO-P1900222.
Funding AgencyGrant Number
Indian Institute of Technology GandhinagarUNSPECIFIED
Caltech Summer Undergraduate Research Fellowship (SURF)UNSPECIFIED
Indo-US Science and Technology ForumJC-001/2017
Subject Keywords:advanced LIGO, gravitational wave detectors, machine learning
Other Numbering System:
Other Numbering System NameOther Numbering System ID
LIGO DocumentP1900222
Issue or Number:17
Record Number:CaltechAUTHORS:20200805-103108228
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Official Citation:A Biswas et al 2020 Class. Quantum Grav. 37 175008
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:104756
Deposited By: Tony Diaz
Deposited On:05 Aug 2020 18:49
Last Modified:05 Aug 2020 18:49

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