Toward real-time detection of unmodeled gravitational wave transients using convolutional neural networks Article Swipe
V. Skliris
,
M. R. Norman
,
P. J. Sutton
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1103/physrevd.110.104034
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1103/physrevd.110.104034
Convolutional neural networks (CNNs) have demonstrated potential for the real-time analysis of data from gravitational wave detector networks for the specific case of signals from coalescing compact-object binaries such as black-hole binaries. Unfortunately, CNNs presented to date have required a precise model of the target signal for training. Such CNNs are therefore not applicable to detecting generic gravitational wave transients from unknown sources, and may be unreliable for anticipated sources such as core-collapse supernovae and long gamma-ray bursts, where unknown physics or computational limitations prevent the development of robust, accurate signal models. We demonstrate for the first time a CNN analysis pipeline with the ability to detect generic signals—those without a precise model—with sensitivity across a wide parameter space and with useful significance. Our CNN has a novel structure that uses not only the network strain data but also the Pearson cross-correlation between detectors to distinguish correlated gravitational wave signals from uncorrelated noise transients. We demonstrate the efficacy of our CNN using data from the second LIGO-Virgo observing run. We show that it has sensitivity approaching that of the “gold-standard” unmodeled transient searches currently used by LIGO-Virgo, at extremely low (order of 1 s) latency and using only a fraction of the computing power required by existing searches, allowing our models the possibility of true real-time detection of gravitational wave transients associated with gamma-ray bursts, core-collapse supernovae, and other relativistic astrophysical phenomena.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1103/physrevd.110.104034
- OA Status
- hybrid
- Cited By
- 12
- References
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- OpenAlex ID
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All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4404414081Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1103/physrevd.110.104034Digital Object Identifier
- Title
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Toward real-time detection of unmodeled gravitational wave transients using convolutional neural networksWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-11-15Full publication date if available
- Authors
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V. Skliris, M. R. Norman, P. J. SuttonList of authors in order
- Landing page
-
https://doi.org/10.1103/physrevd.110.104034Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
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https://doi.org/10.1103/physrevd.110.104034Direct OA link when available
- Concepts
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Convolutional neural network, Gravitational wave, Computer science, Physics, Pattern recognition (psychology), Artificial intelligence, Acoustics, AstronomyTop concepts (fields/topics) attached by OpenAlex
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12Total citation count in OpenAlex
- Citations by year (recent)
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2025: 12Per-year citation counts (last 5 years)
- References (count)
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94Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works_count | 94 |
| abstract_inverted_index.1 | 192 |
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