NNETFIX: an artificial neural network-based denoising engine for gravitational-wave signals Article Swipe
K. Mogushi
,
R. Quitzow-James
,
M. Cavaglià
,
Sumeet Kulkarni
,
F. J. Hayes
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1088/2632-2153/abea69
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1088/2632-2153/abea69
Instrumental and environmental transient noise bursts in gravitational-wave (GW) detectors, or glitches, may impair astrophysical observations by adversely affecting the sky localization and the parameter estimation of GW signals. Denoising of detector data is especially relevant during low-latency operations because electromagnetic follow-up of candidate detections requires accurate, rapid sky localization and inference of astrophysical sources. NNETFIX is a machine learning, artificial neural network-based algorithm designed to estimate the data containing a transient GW signal with an overlapping glitch as though the glitch was absent. The sky localization calculated from the denoised data may be significantly more accurate than the sky localization obtained from the original data or by removing the portion of the data impacted by the glitch. We test NNETFIX in simulated scenarios of binary black hole coalescence signals and discuss the potential for its use in future low-latency LIGO-Virgo-KAGRA searches. In the majority of cases for signals with a high signal-to-noise ratio, we find that the overlap of the sky maps obtained with the denoised data and the original data is better than the overlap of the sky maps obtained with the original data and the data with the glitch removed.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1088/2632-2153/abea69
- https://iopscience.iop.org/article/10.1088/2632-2153/abea69/pdf
- OA Status
- gold
- Cited By
- 1
- References
- 37
- Related Works
- 20
- OpenAlex ID
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3119483553Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/2632-2153/abea69Digital Object Identifier
- Title
-
NNETFIX: an artificial neural network-based denoising engine for gravitational-wave signalsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-02-26Full publication date if available
- Authors
-
K. Mogushi, R. Quitzow-James, M. Cavaglià, Sumeet Kulkarni, F. J. HayesList of authors in order
- Landing page
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https://doi.org/10.1088/2632-2153/abea69Publisher landing page
- PDF URL
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https://iopscience.iop.org/article/10.1088/2632-2153/abea69/pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://iopscience.iop.org/article/10.1088/2632-2153/abea69/pdfDirect OA link when available
- Concepts
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Glitch, Sky, Gravitational wave, Detector, Physics, LIGO, Computer science, Noise (video), Artificial neural network, Noise reduction, SIGNAL (programming language), Artificial intelligence, Astronomy, Optics, Programming language, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2021: 1Per-year citation counts (last 5 years)
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- Related works (count)
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20Other works algorithmically related by OpenAlex
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| publication_date | 2021-02-26 |
| publication_year | 2021 |
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