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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.17587 |
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| _version_ | 1866918282098900992 |
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| author | Ashton, Gregory Malz, Ann-Kristin Colombo, Nicolo |
| author_facet | Ashton, Gregory Malz, Ann-Kristin Colombo, Nicolo |
| contents | Gravitational-wave data from advanced-era interferometric detectors consists of background Gaussian noise, frequent transient artefacts, and rare astrophysical signals. Multiple search algorithms exist to detect the signals from compact binary coalescences, but their varying performance complicates interpretation. We present a machine learning-driven approach that combines results from individual pipelines and utilises conformal prediction to provide robust, calibrated uncertainty quantification. Using simulations, we demonstrate improved detection efficiency and apply our model to GWTC-3, enhancing confidence in multi-pipeline detections, such as the sub-threshold binary neutron star candidate GW200311_103121. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_17587 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing gravitational-wave detection: a machine learning pipeline combination approach with robust uncertainty quantification Ashton, Gregory Malz, Ann-Kristin Colombo, Nicolo General Relativity and Quantum Cosmology High Energy Astrophysical Phenomena Gravitational-wave data from advanced-era interferometric detectors consists of background Gaussian noise, frequent transient artefacts, and rare astrophysical signals. Multiple search algorithms exist to detect the signals from compact binary coalescences, but their varying performance complicates interpretation. We present a machine learning-driven approach that combines results from individual pipelines and utilises conformal prediction to provide robust, calibrated uncertainty quantification. Using simulations, we demonstrate improved detection efficiency and apply our model to GWTC-3, enhancing confidence in multi-pipeline detections, such as the sub-threshold binary neutron star candidate GW200311_103121. |
| title | Enhancing gravitational-wave detection: a machine learning pipeline combination approach with robust uncertainty quantification |
| topic | General Relativity and Quantum Cosmology High Energy Astrophysical Phenomena |
| url | https://arxiv.org/abs/2504.17587 |