Walker, Marissa and Agnew, Alfonso F. and Bidler, Jeffrey and Lundgren, Andrew and Macedo, Alexandra and Macleod, Duncan and Massinger, T. J. and Patane, Oliver and Smith, Joshua R. (2018) Identifying correlations between LIGO’s astronomical range and auxiliary sensors using lasso regression. Classical and Quantum Gravity, 35 (22). Art. No. 225002. ISSN 0264-9381. doi:10.1088/1361-6382/aae593. https://resolver.caltech.edu/CaltechAUTHORS:20181023-094539739
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Abstract
The range to which the Laser Interferometer Gravitational-Wave Observatory (LIGO) can observe astrophysical systems varies over time, limited by noise in the instruments and their environments. Identifying and removing the sources of noise that limit LIGO's range enables higher signal-to-noise observations and increases the number of observations. The LIGO observatories are continuously monitored by hundreds of thousands of auxiliary channels that may contain information about these noise sources. This paper describes an algorithm that uses linear regression, namely lasso (least absolute shrinkage and selection operator) regression, to analyze all of these channels and identify a small subset of them that can be used to reconstruct variations in LIGO's astrophysical range. Exemplary results of the application of this method to three different periods of LIGO Livingston data are presented, along with computational performance and current limitations.
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| Additional Information: | © 2018 IOP Publishing Ltd. Received 6 July 2018; Accepted 2 October 2018; Accepted Manuscript online 2 October 2018; Published 19 October 2018. The authors are grateful to their colleagues in the LIGO Scientific Collaboration for stimulating discussions and for review of this manuscript, especially Gabriele Vajente for helpful comments in the internal review process. The authors would also like to thank the CQG referees for insightful suggestions in their reviews, which have led to improvements in the manuscript and algorithm. MW and JS are pleased to acknowledge the support of Dan O Black and family. This research was supported by National Science Foundation grants NSF PHY-1255650, AST-1559694, PHY-1708035, and PHY-1807069. Computations in this paper were performed on the LIGO Data Grid. | ||||||||||||
| Group: | LIGO | ||||||||||||
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| Subject Keywords: | gravitational wave detectors, machine learning, regression | ||||||||||||
| Issue or Number: | 22 | ||||||||||||
| DOI: | 10.1088/1361-6382/aae593 | ||||||||||||
| Record Number: | CaltechAUTHORS:20181023-094539739 | ||||||||||||
| Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20181023-094539739 | ||||||||||||
| Official Citation: | Marissa Walker et al 2018 Class. Quantum Grav. 35 225002 | ||||||||||||
| Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||||||||
| ID Code: | 90352 | ||||||||||||
| Collection: | CaltechAUTHORS | ||||||||||||
| Deposited By: | Tony Diaz | ||||||||||||
| Deposited On: | 23 Oct 2018 19:57 | ||||||||||||
| Last Modified: | 12 Jul 2022 19:43 |
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