Bayesian Inference for Gravitational Waves from Binary Neutron Star Mergers in Third Generation Observatories
Third generation (3G) gravitational-wave detectors will observe thousands of coalescing neutron star binaries with unprecedented fidelity. Extracting the highest precision science from these signals is expected to be challenging owing to both high signal-to-noise ratios and long-duration signals. We...
| Main Authors: | , , , , , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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American Physical Society (APS)
2022
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| Online Access: | https://hdl.handle.net/1721.1/142300 |
| _version_ | 1811086060505006080 |
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| author | Smith, Rory Borhanian, Ssohrab Sathyaprakash, Bangalore Hernandez Vivanco, Francisco Field, Scott E Lasky, Paul Mandel, Ilya Morisaki, Soichiro Ottaway, David Slagmolen, Bram JJ Thrane, Eric Töyrä, Daniel Vitale, Salvatore |
| author2 | Massachusetts Institute of Technology. Department of Physics |
| author_facet | Massachusetts Institute of Technology. Department of Physics Smith, Rory Borhanian, Ssohrab Sathyaprakash, Bangalore Hernandez Vivanco, Francisco Field, Scott E Lasky, Paul Mandel, Ilya Morisaki, Soichiro Ottaway, David Slagmolen, Bram JJ Thrane, Eric Töyrä, Daniel Vitale, Salvatore |
| author_sort | Smith, Rory |
| collection | MIT |
| description | Third generation (3G) gravitational-wave detectors will observe thousands of coalescing neutron star binaries with unprecedented fidelity. Extracting the highest precision science from these signals is expected to be challenging owing to both high signal-to-noise ratios and long-duration signals. We demonstrate that current Bayesian inference paradigms can be extended to the analysis of binary neutron star signals without breaking the computational bank. We construct reduced-order models for ∼90-min-long gravitational-wave signals covering the observing band (5-2048 Hz), speeding up inference by a factor of ∼1.3×10^{4} compared to the calculation times without reduced-order models. The reduced-order models incorporate key physics including the effects of tidal deformability, amplitude modulation due to Earth's rotation, and spin-induced orbital precession. We show how reduced-order modeling can accelerate inference on data containing multiple overlapping gravitational-wave signals, and determine the speedup as a function of the number of overlapping signals. Thus, we conclude that Bayesian inference is computationally tractable for the long-lived, overlapping, high signal-to-noise-ratio events present in 3G observatories. |
| first_indexed | 2024-09-23T13:20:00Z |
| format | Article |
| id | mit-1721.1/142300 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T13:20:00Z |
| publishDate | 2022 |
| publisher | American Physical Society (APS) |
| record_format | dspace |
| spelling | mit-1721.1/1423002023-02-03T21:15:52Z Bayesian Inference for Gravitational Waves from Binary Neutron Star Mergers in Third Generation Observatories Smith, Rory Borhanian, Ssohrab Sathyaprakash, Bangalore Hernandez Vivanco, Francisco Field, Scott E Lasky, Paul Mandel, Ilya Morisaki, Soichiro Ottaway, David Slagmolen, Bram JJ Thrane, Eric Töyrä, Daniel Vitale, Salvatore Massachusetts Institute of Technology. Department of Physics MIT Kavli Institute for Astrophysics and Space Research LIGO (Observatory : Massachusetts Institute of Technology) Third generation (3G) gravitational-wave detectors will observe thousands of coalescing neutron star binaries with unprecedented fidelity. Extracting the highest precision science from these signals is expected to be challenging owing to both high signal-to-noise ratios and long-duration signals. We demonstrate that current Bayesian inference paradigms can be extended to the analysis of binary neutron star signals without breaking the computational bank. We construct reduced-order models for ∼90-min-long gravitational-wave signals covering the observing band (5-2048 Hz), speeding up inference by a factor of ∼1.3×10^{4} compared to the calculation times without reduced-order models. The reduced-order models incorporate key physics including the effects of tidal deformability, amplitude modulation due to Earth's rotation, and spin-induced orbital precession. We show how reduced-order modeling can accelerate inference on data containing multiple overlapping gravitational-wave signals, and determine the speedup as a function of the number of overlapping signals. Thus, we conclude that Bayesian inference is computationally tractable for the long-lived, overlapping, high signal-to-noise-ratio events present in 3G observatories. 2022-05-04T14:24:00Z 2022-05-04T14:24:00Z 2021 2022-05-04T14:18:05Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/142300 Smith, Rory, Borhanian, Ssohrab, Sathyaprakash, Bangalore, Hernandez Vivanco, Francisco, Field, Scott E et al. 2021. "Bayesian Inference for Gravitational Waves from Binary Neutron Star Mergers in Third Generation Observatories." Physical Review Letters, 127 (8). en 10.1103/PHYSREVLETT.127.081102 Physical Review Letters Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf American Physical Society (APS) APS |
| spellingShingle | Smith, Rory Borhanian, Ssohrab Sathyaprakash, Bangalore Hernandez Vivanco, Francisco Field, Scott E Lasky, Paul Mandel, Ilya Morisaki, Soichiro Ottaway, David Slagmolen, Bram JJ Thrane, Eric Töyrä, Daniel Vitale, Salvatore Bayesian Inference for Gravitational Waves from Binary Neutron Star Mergers in Third Generation Observatories |
| title | Bayesian Inference for Gravitational Waves from Binary Neutron Star Mergers in Third Generation Observatories |
| title_full | Bayesian Inference for Gravitational Waves from Binary Neutron Star Mergers in Third Generation Observatories |
| title_fullStr | Bayesian Inference for Gravitational Waves from Binary Neutron Star Mergers in Third Generation Observatories |
| title_full_unstemmed | Bayesian Inference for Gravitational Waves from Binary Neutron Star Mergers in Third Generation Observatories |
| title_short | Bayesian Inference for Gravitational Waves from Binary Neutron Star Mergers in Third Generation Observatories |
| title_sort | bayesian inference for gravitational waves from binary neutron star mergers in third generation observatories |
| url | https://hdl.handle.net/1721.1/142300 |
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