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Identifying correlations between LIGO’s astronomical range and auxiliary sensors using lasso regression

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. http://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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1088/1361-6382/aae593DOIArticle
https://arxiv.org/abs/1807.02592arXivDiscussion Paper
ORCID:
AuthorORCID
Walker, Marissa0000-0002-7176-6914
Massinger, T. J.0000-0002-3429-5025
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
Funders:
Funding AgencyGrant Number
Dan O. Black and FamilyUNSPECIFIED
NSFPHY-1255650
NSFAST-1559694
NSFPHY-1708035
NSFPHY-1807069
Subject Keywords:gravitational wave detectors, machine learning, regression
Record Number:CaltechAUTHORS:20181023-094539739
Persistent URL:http://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:23 Oct 2018 19:57

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