Decoding Long-duration Gravitational Waves from Binary Neutron Stars with Machine Learning: Parameter Estimation and Equations of State
Gravitational waves (GWs) from binary neutron stars (BNSs) offer a valuable understanding of the nature of compact objects and hadronic matter, and the science potential will be greatly enhanced by the third-generation (3G) GW detectors, which are expected to detect BNS signals with order-of-magnitu...
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IOP Publishing
2025-01-01
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| Series: | The Astrophysical Journal Letters |
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| Online Access: | https://doi.org/10.3847/2041-8213/ade42f |
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| author | Qian Hu Jessica Irwin Qi Sun Christopher Messenger Lami Suleiman Ik Siong Heng John Veitch |
| author_facet | Qian Hu Jessica Irwin Qi Sun Christopher Messenger Lami Suleiman Ik Siong Heng John Veitch |
| author_sort | Qian Hu |
| collection | DOAJ |
| description | Gravitational waves (GWs) from binary neutron stars (BNSs) offer a valuable understanding of the nature of compact objects and hadronic matter, and the science potential will be greatly enhanced by the third-generation (3G) GW detectors, which are expected to detect BNS signals with order-of-magnitude improvements in duration, detection rates, and signal strength. However, the resulting computational demands for analyzing such prolonged signals pose a critical challenge that existing Bayesian methods cannot feasibly address in the 3G era. To bridge this critical gap, we demonstrate a machine learning–based workflow capable of producing source parameter estimation and constraints on equations of state (EOSs) for hours-long BNS signals in seconds with minimal hardware costs. We employ efficient compression of the GW data and EOS using neural networks, based on which we build normalizing flows for inference that can deliver results in seconds. The optimized computational cost of BNS signal analysis with our framework shows that machine learning has the potential to be an indispensable tool for future catalog-level BNS analyses, paving the way for large-scale investigations of BNS-related physics across the 3G observational landscape. |
| format | Article |
| id | doaj-art-ab34db5e08554fa0a54a032a4cbe2d74 |
| institution | Kabale University |
| issn | 2041-8205 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal Letters |
| spelling | doaj-art-ab34db5e08554fa0a54a032a4cbe2d742025-08-20T03:28:01ZengIOP PublishingThe Astrophysical Journal Letters2041-82052025-01-019871L1710.3847/2041-8213/ade42fDecoding Long-duration Gravitational Waves from Binary Neutron Stars with Machine Learning: Parameter Estimation and Equations of StateQian Hu0https://orcid.org/0000-0002-3033-6491Jessica Irwin1https://orcid.org/0000-0002-2364-2191Qi Sun2https://orcid.org/0009-0004-5204-765XChristopher Messenger3https://orcid.org/0000-0001-7488-5022Lami Suleiman4https://orcid.org/0000-0003-3783-7448Ik Siong Heng5https://orcid.org/0000-0002-1977-0019John Veitch6https://orcid.org/0000-0002-6508-0713Institute for Gravitational Research, School of Physics and Astronomy, University of Glasgow , Glasgow, G12 8QQ, UK ; Qian.Hu@glasgow.ac.uk, John.Veitch@glasgow.ac.ukInstitute for Gravitational Research, School of Physics and Astronomy, University of Glasgow , Glasgow, G12 8QQ, UK ; Qian.Hu@glasgow.ac.uk, John.Veitch@glasgow.ac.ukDepartment of Computer Science, City University of Hong Kong , Tat Chee Avenue, Kowloon, Hong Kong SAR, Hong KongInstitute for Gravitational Research, School of Physics and Astronomy, University of Glasgow , Glasgow, G12 8QQ, UK ; Qian.Hu@glasgow.ac.uk, John.Veitch@glasgow.ac.ukNicholas and Lee Begovich Center for Gravitational Wave Physics and Astronomy, California State University Fullerton , Fullerton, CA 92831, USAInstitute for Gravitational Research, School of Physics and Astronomy, University of Glasgow , Glasgow, G12 8QQ, UK ; Qian.Hu@glasgow.ac.uk, John.Veitch@glasgow.ac.ukInstitute for Gravitational Research, School of Physics and Astronomy, University of Glasgow , Glasgow, G12 8QQ, UK ; Qian.Hu@glasgow.ac.uk, John.Veitch@glasgow.ac.ukGravitational waves (GWs) from binary neutron stars (BNSs) offer a valuable understanding of the nature of compact objects and hadronic matter, and the science potential will be greatly enhanced by the third-generation (3G) GW detectors, which are expected to detect BNS signals with order-of-magnitude improvements in duration, detection rates, and signal strength. However, the resulting computational demands for analyzing such prolonged signals pose a critical challenge that existing Bayesian methods cannot feasibly address in the 3G era. To bridge this critical gap, we demonstrate a machine learning–based workflow capable of producing source parameter estimation and constraints on equations of state (EOSs) for hours-long BNS signals in seconds with minimal hardware costs. We employ efficient compression of the GW data and EOS using neural networks, based on which we build normalizing flows for inference that can deliver results in seconds. The optimized computational cost of BNS signal analysis with our framework shows that machine learning has the potential to be an indispensable tool for future catalog-level BNS analyses, paving the way for large-scale investigations of BNS-related physics across the 3G observational landscape.https://doi.org/10.3847/2041-8213/ade42fGravitational wavesGravitational wave astronomyNeutron starsNuclear astrophysicsAstronomy data analysis |
| spellingShingle | Qian Hu Jessica Irwin Qi Sun Christopher Messenger Lami Suleiman Ik Siong Heng John Veitch Decoding Long-duration Gravitational Waves from Binary Neutron Stars with Machine Learning: Parameter Estimation and Equations of State The Astrophysical Journal Letters Gravitational waves Gravitational wave astronomy Neutron stars Nuclear astrophysics Astronomy data analysis |
| title | Decoding Long-duration Gravitational Waves from Binary Neutron Stars with Machine Learning: Parameter Estimation and Equations of State |
| title_full | Decoding Long-duration Gravitational Waves from Binary Neutron Stars with Machine Learning: Parameter Estimation and Equations of State |
| title_fullStr | Decoding Long-duration Gravitational Waves from Binary Neutron Stars with Machine Learning: Parameter Estimation and Equations of State |
| title_full_unstemmed | Decoding Long-duration Gravitational Waves from Binary Neutron Stars with Machine Learning: Parameter Estimation and Equations of State |
| title_short | Decoding Long-duration Gravitational Waves from Binary Neutron Stars with Machine Learning: Parameter Estimation and Equations of State |
| title_sort | decoding long duration gravitational waves from binary neutron stars with machine learning parameter estimation and equations of state |
| topic | Gravitational waves Gravitational wave astronomy Neutron stars Nuclear astrophysics Astronomy data analysis |
| url | https://doi.org/10.3847/2041-8213/ade42f |
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