NNETFIX: An artificial neural network-based denoising engine for\n 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/10.48550/arxiv.2101.04712
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2101.04712
Instrumental and environmental transient noise bursts in gravitational-wave\ndetectors, or glitches, may impair astrophysical observations by adversely\naffecting the sky localization and the parameter estimation of\ngravitational-wave signals. Denoising of detector data is especially relevant\nduring low-latency operations because electromagnetic follow-up of candidate\ndetections requires accurate, rapid sky localization and inference of\nastrophysical sources. NNETFIX is a machine learning-based algorithm designed\nto remove glitches detected in coincidence with transient gravitational-wave\nsignals. NNETFIX uses artificial neural networks to estimate the portion of the\ndata lost due to the presence of the glitch, which allows the recalculation of\nthe sky localization of the astrophysical signal. The sky localization of the\ndenoised data may be significantly more accurate than the sky localization\nobtained from the original data or by removing the portion of the data impacted\nby the glitch. We test NNETFIX in simulated scenarios of binary black hole\ncoalescence signals and discuss the potential for its use in future low-latency\nLIGO-Virgo-KAGRA searches. In the majority of cases for signals with a high\nsignal-to-noise ratio, we find that the overlap of the sky maps obtained with\nthe denoised data and the original data is better than the overlap of the sky\nmaps obtained with the original data and the data with the glitch removed.\n
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- Type
- preprint
- Landing Page
- http://arxiv.org/abs/2101.04712
- https://arxiv.org/pdf/2101.04712
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4287391383
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https://openalex.org/W4287391383Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2101.04712Digital Object Identifier
- Title
-
NNETFIX: An artificial neural network-based denoising engine for\n gravitational-wave signalsWork title
- Type
-
preprintOpenAlex work type
- Publication year
-
2021Year of publication
- Publication date
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2021-01-12Full publication date if available
- Authors
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K. Mogushi, R. Quitzow-James, M. Cavaglià, Sumeet Kulkarni, F. J. HayesList of authors in order
- Landing page
-
https://arxiv.org/abs/2101.04712Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2101.04712Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2101.04712Direct OA link when available
- Concepts
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Glitch, Sky, Gravitational wave, Detector, Physics, LIGO, Artificial neural network, Computer science, Noise reduction, Noise (video), SIGNAL (programming language), Artificial intelligence, Astrophysics, Optics, Programming language, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.relevant\nduring | 31 |
| abstract_inverted_index.hole\ncoalescence | 132 |
| abstract_inverted_index.of\nastrophysical | 46 |
| abstract_inverted_index.adversely\naffecting | 15 |
| abstract_inverted_index.candidate\ndetections | 38 |
| abstract_inverted_index.high\nsignal-to-noise | 154 |
| abstract_inverted_index.localization\nobtained | 107 |
| abstract_inverted_index.of\ngravitational-wave | 23 |
| abstract_inverted_index.gravitational-wave\nsignals. | 62 |
| abstract_inverted_index.low-latency\nLIGO-Virgo-KAGRA | 143 |
| abstract_inverted_index.gravitational-wave\ndetectors, | 7 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.2278807 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |