PINNGraPE: Physics Informed Neural Network for Gravitational wave Parameter Estimation
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911190710484992 |
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| author | Smith, Leigh Scialpi, Matteo di Clemente, Francesco Bejger, Michał |
| author_facet | Smith, Leigh Scialpi, Matteo di Clemente, Francesco Bejger, Michał |
| contents | Weakly-modelled searches for gravitational waves are essential for ensuring that all potential sources are accounted for in detection efforts, as they make minimal assumptions regarding source morphology. While these searches primarily target generic transient sources, they are also highly effective at identifying a broad range of compact binary coalescences, demonstrated by the weakly-modelled search algorithm coherent WaveBurst being the first to detect GW150914. Despite their ability to detect compact binaries with diverse properties, the accurate estimation of source parameters from their output remains to be a challenging task. To overcome this, we leverage physics-informed neural networks, which serve as a powerful tool for parameter estimation by applying physical constraints through the universal differential equation governing a compact binary system. With this approach, we rapidly infer the mass parameters of binary black hole merger systems to within 7% from only the time-frequency representation of the gravitational wave signal. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_02858 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PINNGraPE: Physics Informed Neural Network for Gravitational wave Parameter Estimation Smith, Leigh Scialpi, Matteo di Clemente, Francesco Bejger, Michał General Relativity and Quantum Cosmology Instrumentation and Methods for Astrophysics Weakly-modelled searches for gravitational waves are essential for ensuring that all potential sources are accounted for in detection efforts, as they make minimal assumptions regarding source morphology. While these searches primarily target generic transient sources, they are also highly effective at identifying a broad range of compact binary coalescences, demonstrated by the weakly-modelled search algorithm coherent WaveBurst being the first to detect GW150914. Despite their ability to detect compact binaries with diverse properties, the accurate estimation of source parameters from their output remains to be a challenging task. To overcome this, we leverage physics-informed neural networks, which serve as a powerful tool for parameter estimation by applying physical constraints through the universal differential equation governing a compact binary system. With this approach, we rapidly infer the mass parameters of binary black hole merger systems to within 7% from only the time-frequency representation of the gravitational wave signal. |
| title | PINNGraPE: Physics Informed Neural Network for Gravitational wave Parameter Estimation |
| topic | General Relativity and Quantum Cosmology Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2510.02858 |