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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.06491 |
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| _version_ | 1866914911287771136 |
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| author | Chan, Man Leong McIver, Jess Mahabal, Ashish Messick, Cody Haggard, Daryl Raza, Nayyer Lecoeuche, Yannick Sutton, Patrick J. Ewing, Becca Di Renzo, Francesco Cabero, Miriam Ng, Raymond Coughlin, Michael W. Ghosh, Shaon Godwin, Patrick |
| author_facet | Chan, Man Leong McIver, Jess Mahabal, Ashish Messick, Cody Haggard, Daryl Raza, Nayyer Lecoeuche, Yannick Sutton, Patrick J. Ewing, Becca Di Renzo, Francesco Cabero, Miriam Ng, Raymond Coughlin, Michael W. Ghosh, Shaon Godwin, Patrick |
| contents | Electromagnetic follow-up observations of gravitational wave events offer critical insights and provide significant scientific gain from this new class of astrophysical transients. Accurate identification of gravitational wave candidates and rapid release of sky localization information are crucial for the success of these electromagnetic follow-up observations. However, searches for gravitational wave candidates in real time suffer a non-negligible false alarm rate. By leveraging the sky localization information and other metadata associated with gravitational wave candidates, GWSkyNet, a machine learning classifier developed by Cabero et al. (2020), demonstrated promising accuracy for the identification of the origin of event candidates. We improve the performance of the classifier for LIGO-Virgo-KAGRA's fourth observing run by reviewing and updating the architecture and features used as inputs by the algorithm. We also retrain and fine-tune the classifier with data from the third observing run. To improve the prospect of electromagnetic follow-up observations, we incorporate GWSkyNet into LIGO-Virgo-KAGRA's low-latency infrastructure as an automatic pipeline for the evaluation of gravitational wave alerts in real time. We test the readiness of the algorithm on a LIGO-Virgo-KAGRA mock data challenge campaign. The results show that by thresholding on the GWSkyNet score, noise masquerading as astrophysical sources can be rejected efficiently and the majority of true astrophysical signals correctly identified. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_06491 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GWSkyNet II : a refined machine learning pipeline for real-time classification of public gravitational wave alerts Chan, Man Leong McIver, Jess Mahabal, Ashish Messick, Cody Haggard, Daryl Raza, Nayyer Lecoeuche, Yannick Sutton, Patrick J. Ewing, Becca Di Renzo, Francesco Cabero, Miriam Ng, Raymond Coughlin, Michael W. Ghosh, Shaon Godwin, Patrick Instrumentation and Methods for Astrophysics Electromagnetic follow-up observations of gravitational wave events offer critical insights and provide significant scientific gain from this new class of astrophysical transients. Accurate identification of gravitational wave candidates and rapid release of sky localization information are crucial for the success of these electromagnetic follow-up observations. However, searches for gravitational wave candidates in real time suffer a non-negligible false alarm rate. By leveraging the sky localization information and other metadata associated with gravitational wave candidates, GWSkyNet, a machine learning classifier developed by Cabero et al. (2020), demonstrated promising accuracy for the identification of the origin of event candidates. We improve the performance of the classifier for LIGO-Virgo-KAGRA's fourth observing run by reviewing and updating the architecture and features used as inputs by the algorithm. We also retrain and fine-tune the classifier with data from the third observing run. To improve the prospect of electromagnetic follow-up observations, we incorporate GWSkyNet into LIGO-Virgo-KAGRA's low-latency infrastructure as an automatic pipeline for the evaluation of gravitational wave alerts in real time. We test the readiness of the algorithm on a LIGO-Virgo-KAGRA mock data challenge campaign. The results show that by thresholding on the GWSkyNet score, noise masquerading as astrophysical sources can be rejected efficiently and the majority of true astrophysical signals correctly identified. |
| title | GWSkyNet II : a refined machine learning pipeline for real-time classification of public gravitational wave alerts |
| topic | Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2408.06491 |