Applications of machine learning in gravitational-wave research with current interferometric detectors
Abstract This article provides an overview of the current state of machine learning in gravitational-wave research with interferometric detectors. Such applications are often still in their early days, but have reached sufficient popularity to warrant an assessment of their impact across various dom...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-02-01
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| Series: | Living Reviews in Relativity |
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| Online Access: | https://doi.org/10.1007/s41114-024-00055-8 |
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| author | Elena Cuoco Marco Cavaglià Ik Siong Heng David Keitel Christopher Messenger |
| author_facet | Elena Cuoco Marco Cavaglià Ik Siong Heng David Keitel Christopher Messenger |
| author_sort | Elena Cuoco |
| collection | DOAJ |
| description | Abstract This article provides an overview of the current state of machine learning in gravitational-wave research with interferometric detectors. Such applications are often still in their early days, but have reached sufficient popularity to warrant an assessment of their impact across various domains, including detector studies, noise and signal simulations, and the detection and interpretation of astrophysical signals. In detector studies, machine learning could be useful to optimize instruments like LIGO, Virgo, KAGRA, and future detectors. Algorithms could predict and help in mitigating environmental disturbances in real time, ensuring detectors operate at peak performance. Furthermore, machine-learning tools for characterizing and cleaning data after it is taken have already become crucial tools for achieving the best sensitivity of the LIGO–Virgo–KAGRA network. In data analysis, machine learning has already been applied as an alternative to traditional methods for signal detection, source localization, noise reduction, and parameter estimation. For some signal types, it can already yield improved efficiency and robustness, though in many other areas traditional methods remain dominant. As the field evolves, the role of machine learning in advancing gravitational-wave research is expected to become increasingly prominent. This report highlights recent advancements, challenges, and perspectives for the current detector generation, with a brief outlook to the next generation of gravitational-wave detectors. |
| format | Article |
| id | doaj-art-c8e8a2d54352434a8134538b9655d06c |
| institution | DOAJ |
| issn | 1433-8351 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Living Reviews in Relativity |
| spelling | doaj-art-c8e8a2d54352434a8134538b9655d06c2025-08-20T03:04:29ZengSpringerOpenLiving Reviews in Relativity1433-83512025-02-0128119110.1007/s41114-024-00055-8Applications of machine learning in gravitational-wave research with current interferometric detectorsElena Cuoco0Marco Cavaglià1Ik Siong Heng2David Keitel3Christopher Messenger4Physics and Astronomy Department (DIFA), Alma Mater Studiorum- Università di BolognaInstitute of Multi-messenger Astrophysics and Cosmology, Missouri University of Science and TechnologySUPA, School of Physics and Astronomy, University of GlasgowDepartament de Física, Universitat de les Illes BalearsSUPA, School of Physics and Astronomy, University of GlasgowAbstract This article provides an overview of the current state of machine learning in gravitational-wave research with interferometric detectors. Such applications are often still in their early days, but have reached sufficient popularity to warrant an assessment of their impact across various domains, including detector studies, noise and signal simulations, and the detection and interpretation of astrophysical signals. In detector studies, machine learning could be useful to optimize instruments like LIGO, Virgo, KAGRA, and future detectors. Algorithms could predict and help in mitigating environmental disturbances in real time, ensuring detectors operate at peak performance. Furthermore, machine-learning tools for characterizing and cleaning data after it is taken have already become crucial tools for achieving the best sensitivity of the LIGO–Virgo–KAGRA network. In data analysis, machine learning has already been applied as an alternative to traditional methods for signal detection, source localization, noise reduction, and parameter estimation. For some signal types, it can already yield improved efficiency and robustness, though in many other areas traditional methods remain dominant. As the field evolves, the role of machine learning in advancing gravitational-wave research is expected to become increasingly prominent. This report highlights recent advancements, challenges, and perspectives for the current detector generation, with a brief outlook to the next generation of gravitational-wave detectors.https://doi.org/10.1007/s41114-024-00055-8Gravitational wavesMachine learningSignal processingInterferometric detectors |
| spellingShingle | Elena Cuoco Marco Cavaglià Ik Siong Heng David Keitel Christopher Messenger Applications of machine learning in gravitational-wave research with current interferometric detectors Living Reviews in Relativity Gravitational waves Machine learning Signal processing Interferometric detectors |
| title | Applications of machine learning in gravitational-wave research with current interferometric detectors |
| title_full | Applications of machine learning in gravitational-wave research with current interferometric detectors |
| title_fullStr | Applications of machine learning in gravitational-wave research with current interferometric detectors |
| title_full_unstemmed | Applications of machine learning in gravitational-wave research with current interferometric detectors |
| title_short | Applications of machine learning in gravitational-wave research with current interferometric detectors |
| title_sort | applications of machine learning in gravitational wave research with current interferometric detectors |
| topic | Gravitational waves Machine learning Signal processing Interferometric detectors |
| url | https://doi.org/10.1007/s41114-024-00055-8 |
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