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  Application of dictionary learning to denoise LIGO's blip noise transients

Torres-Forne, A., Cuoco, E., Font, J. A., & Marquina, A. (2020). Application of dictionary learning to denoise LIGO's blip noise transients. Physical Review D, 102: 023011. doi:10.1103/PhysRevD.102.023011.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0006-C145-2 Version Permalink: http://hdl.handle.net/21.11116/0000-0006-C149-E
Genre: Journal Article

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 Creators:
Torres-Forne, Alejandro1, Author              
Cuoco, Elena, Author
Font, José A., Author
Marquina, Antonio, Author
Affiliations:
1Computational Relativistic Astrophysics, AEI-Golm, MPI for Gravitational Physics, Max Planck Society, ou_2541714              

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Free keywords: General Relativity and Quantum Cosmology, gr-qc
 Abstract: Data streams of gravitational-wave detectors are polluted by transient noise features, or "glitches", of instrumental and environmental origin. In this work, we investigate the use of total-variation methods and learned dictionaries to mitigate the effect of those transients in the data. We focus on a specific type of transient, "blip" glitches, as this is the most common type of glitch present in the LIGO detectors and their waveforms are easy to identify. We randomly select 80 blip glitches scattered in the data from advanced LIGO's O1 run, as provided by the citizen-science project Gravity Spy. Our results show that dictionary-learning methods are a valid approach to model and subtract most of the glitch contribution in all cases analyzed, particularly at frequencies below $\sim 1$ kHz. The high-frequency component of the glitch is best removed when a combination of dictionaries with different atom length is employed. As a further example, we apply our approach to the glitch visible in the LIGO-Livingston data around the time of merger of binary neutron star signal GW170817, finding satisfactory results. This paper is the first step in our ongoing program to automatically classify and subtract all families of gravitational-wave glitches employing variational methods.

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 Dates: 2020-02-262020-03-302020
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: arXiv: 2002.11668
DOI: 10.1103/PhysRevD.102.023011
URI: http://arxiv.org/abs/2002.11668
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Title: Physical Review D
  Other : Phys. Rev. D.
Source Genre: Journal
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Publ. Info: Lancaster, Pa. : American Physical Society
Pages: - Volume / Issue: 102 Sequence Number: 023011 Start / End Page: - Identifier: ISSN: 0556-2821
CoNE: https://pure.mpg.de/cone/journals/resource/111088197762258