Transient classification in LIGO data using difference boosting neural network Article Swipe
N. Mukund
,
Sheelu Abraham
,
S. Kandhasamy
,
S. Mitra
,
Ninan Sajeeth Philip
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1103/physrevd.95.104059
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1103/physrevd.95.104059
Detection and classification of transients in data from gravitational wave\ndetectors are crucial for efficient searches for true astrophysical events and\nidentification of noise sources. We present a hybrid method for classification\nof short duration transients seen in gravitational wave data using both\nsupervised and unsupervised machine learning techniques. To train the\nclassifiers we use the relative wavelet energy and the corresponding entropy\nobtained by applying one-dimensional wavelet decomposition on the data. The\nprediction accuracy of the trained classifier on 9 simulated classes of\ngravitational wave transients and also LIGO's sixth science run hardware\ninjections are reported. Targeted searches for a couple of known classes of\nnon-astrophysical signals in the first observational run of Advanced LIGO data\nare also presented. The ability to accurately identify transient classes using\nminimal training samples makes the proposed method a useful tool for LIGO\ndetector characterization as well as searches for short duration gravitational\nwave signals.\n
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1103/physrevd.95.104059
- OA Status
- green
- Cited By
- 72
- References
- 42
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W2526275824Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1103/physrevd.95.104059Digital Object Identifier
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Transient classification in LIGO data using difference boosting neural networkWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2017Year of publication
- Publication date
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2017-05-31Full publication date if available
- Authors
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N. Mukund, Sheelu Abraham, S. Kandhasamy, S. Mitra, Ninan Sajeeth PhilipList of authors in order
- Landing page
-
https://doi.org/10.1103/physrevd.95.104059Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/1609.07259Direct OA link when available
- Concepts
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Boosting (machine learning), LIGO, Artificial neural network, Computer science, Transient (computer programming), Artificial intelligence, Pattern recognition (psychology), Machine learning, Telecommunications, Operating system, DetectorTop concepts (fields/topics) attached by OpenAlex
- Cited by
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72Total citation count in OpenAlex
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2025: 3, 2024: 5, 2023: 10, 2022: 13, 2021: 8Per-year citation counts (last 5 years)
- References (count)
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42Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| primary_location.landing_page_url | https://doi.org/10.1103/physrevd.95.104059 |
| publication_date | 2017-05-31 |
| publication_year | 2017 |
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