Real-time gravitational-wave inference for binary neutron stars using machine learning
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916646608699392 |
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| author | Dax, Maximilian Green, Stephen R. Gair, Jonathan Gupte, Nihar Pürrer, Michael Raymond, Vivien Wildberger, Jonas Macke, Jakob H. Buonanno, Alessandra Schölkopf, Bernhard |
| author_facet | Dax, Maximilian Green, Stephen R. Gair, Jonathan Gupte, Nihar Pürrer, Michael Raymond, Vivien Wildberger, Jonas Macke, Jakob H. Buonanno, Alessandra Schölkopf, Bernhard |
| contents | Mergers of binary neutron stars (BNSs) emit signals in both the gravitational-wave (GW) and electromagnetic (EM) spectra. Famously, the 2017 multi-messenger observation of GW170817 led to scientific discoveries across cosmology, nuclear physics, and gravity. Central to these results were the sky localization and distance obtained from GW data, which, in the case of GW170817, helped to identify the associated EM transient, AT 2017gfo, 11 hours after the GW signal. Fast analysis of GW data is critical for directing time-sensitive EM observations; however, due to challenges arising from the length and complexity of signals, it is often necessary to make approximations that sacrifice accuracy. Here, we present a machine learning framework that performs complete BNS inference in just one second without making any such approximations. Our approach enhances multi-messenger observations by providing (i) accurate localization even before the merger; (ii) improved localization precision by $\sim30\%$ compared to approximate low-latency methods; and (iii) detailed information on luminosity distance, inclination, and masses, which can be used to prioritize expensive telescope time. Additionally, the flexibility and reduced cost of our method open new opportunities for equation-of-state studies. Finally, we demonstrate that our method scales to extremely long signals, up to an hour in length, thus serving as a blueprint for data analysis for next-generation ground- and space-based detectors. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_09602 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Real-time gravitational-wave inference for binary neutron stars using machine learning Dax, Maximilian Green, Stephen R. Gair, Jonathan Gupte, Nihar Pürrer, Michael Raymond, Vivien Wildberger, Jonas Macke, Jakob H. Buonanno, Alessandra Schölkopf, Bernhard General Relativity and Quantum Cosmology Instrumentation and Methods for Astrophysics Machine Learning Mergers of binary neutron stars (BNSs) emit signals in both the gravitational-wave (GW) and electromagnetic (EM) spectra. Famously, the 2017 multi-messenger observation of GW170817 led to scientific discoveries across cosmology, nuclear physics, and gravity. Central to these results were the sky localization and distance obtained from GW data, which, in the case of GW170817, helped to identify the associated EM transient, AT 2017gfo, 11 hours after the GW signal. Fast analysis of GW data is critical for directing time-sensitive EM observations; however, due to challenges arising from the length and complexity of signals, it is often necessary to make approximations that sacrifice accuracy. Here, we present a machine learning framework that performs complete BNS inference in just one second without making any such approximations. Our approach enhances multi-messenger observations by providing (i) accurate localization even before the merger; (ii) improved localization precision by $\sim30\%$ compared to approximate low-latency methods; and (iii) detailed information on luminosity distance, inclination, and masses, which can be used to prioritize expensive telescope time. Additionally, the flexibility and reduced cost of our method open new opportunities for equation-of-state studies. Finally, we demonstrate that our method scales to extremely long signals, up to an hour in length, thus serving as a blueprint for data analysis for next-generation ground- and space-based detectors. |
| title | Real-time gravitational-wave inference for binary neutron stars using machine learning |
| topic | General Relativity and Quantum Cosmology Instrumentation and Methods for Astrophysics Machine Learning |
| url | https://arxiv.org/abs/2407.09602 |