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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2405.17721 |
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| _version_ | 1866909546367156224 |
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| author | Badger, Charles Srinivasan, Rahul Torres-Forné, Alejandro Bizouard, Marie Anne Font, José A. Sakellariadou, Mairi Lamberts, Astrid |
| author_facet | Badger, Charles Srinivasan, Rahul Torres-Forné, Alejandro Bizouard, Marie Anne Font, José A. Sakellariadou, Mairi Lamberts, Astrid |
| contents | Current gravitational wave (GW) detection pipelines for compact binary coalescence based on matched-filtering have reported over 90 confident detections during the first three observing runs of the LIGO-Virgo-KAGRA (LVK) detector network. Decreasing the latency of detection, in particular for future detectors anticipated to have high detection rates, remains an ongoing effort. In this paper, we develop and test a sparse dictionary learning (SDL) algorithm for the rapid detection of GWs. We evaluate the algorithms biases and estimate its GW detection rate for an astrophysical population of binary black holes. The SDL algorithm is assessed using both, simulated data injected into the proposed A+ detector sensitivity and real data containing confident detections from the third LVK observing run. We find that our SDL algorithm can reconstruct a single binary black hole signal in less than 1 s. This suggests that SDL could be regarded as a promising approach for rapid, efficient GW detection in future observing runs of ground-based detectors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_17721 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Rapid detection of gravitational waves from binary black hole mergers using sparse dictionary learning Badger, Charles Srinivasan, Rahul Torres-Forné, Alejandro Bizouard, Marie Anne Font, José A. Sakellariadou, Mairi Lamberts, Astrid General Relativity and Quantum Cosmology High Energy Astrophysical Phenomena Current gravitational wave (GW) detection pipelines for compact binary coalescence based on matched-filtering have reported over 90 confident detections during the first three observing runs of the LIGO-Virgo-KAGRA (LVK) detector network. Decreasing the latency of detection, in particular for future detectors anticipated to have high detection rates, remains an ongoing effort. In this paper, we develop and test a sparse dictionary learning (SDL) algorithm for the rapid detection of GWs. We evaluate the algorithms biases and estimate its GW detection rate for an astrophysical population of binary black holes. The SDL algorithm is assessed using both, simulated data injected into the proposed A+ detector sensitivity and real data containing confident detections from the third LVK observing run. We find that our SDL algorithm can reconstruct a single binary black hole signal in less than 1 s. This suggests that SDL could be regarded as a promising approach for rapid, efficient GW detection in future observing runs of ground-based detectors. |
| title | Rapid detection of gravitational waves from binary black hole mergers using sparse dictionary learning |
| topic | General Relativity and Quantum Cosmology High Energy Astrophysical Phenomena |
| url | https://arxiv.org/abs/2405.17721 |