Detection and parameter estimation of gravitational waves from binary neutron-star mergers in real LIGO data using deep learning
One of the key challenges of real-time detection and parameter estimation of gravitational waves from compact binary mergers is the computational cost of conventional matched-filtering and Bayesian inference approaches. In particular, the application of these methods to the full signal parameter spa...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-04-01
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| Series: | Physics Letters B |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0370269321001015 |
| _version_ | 1818721444915314688 |
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| author | Plamen G. Krastev Kiranjyot Gill V. Ashley Villar Edo Berger |
| author_facet | Plamen G. Krastev Kiranjyot Gill V. Ashley Villar Edo Berger |
| author_sort | Plamen G. Krastev |
| collection | DOAJ |
| description | One of the key challenges of real-time detection and parameter estimation of gravitational waves from compact binary mergers is the computational cost of conventional matched-filtering and Bayesian inference approaches. In particular, the application of these methods to the full signal parameter space available to the gravitational-wave detectors, and/or real-time parameter estimation is computationally prohibitive. On the other hand, rapid detection and inference are critical for prompt follow-up of the electromagnetic and astro-particle counterparts accompanying important transients, such as binary neutron-star and black-hole neutron-star mergers. Training deep neural networks to identify specific signals and learn a computationally efficient representation of the mapping between gravitational-wave signals and their parameters allows both detection and inference to be done quickly and reliably, with high sensitivity and accuracy. In this work we apply a deep-learning approach to rapidly identify and characterize transient gravitational-wave signals from binary neutron-star mergers in real LIGO data. We show for the first time that artificial neural networks can promptly detect and characterize binary neutron star gravitational-wave signals in real LIGO data, and distinguish them from noise and signals from coalescing black-hole binaries. We illustrate this key result by demonstrating that our deep-learning framework classifies correctly all gravitational-wave events from the Gravitational-Wave Transient Catalog, GWTC-1 [Abbott et al. (2019) [4]]. These results emphasize the importance of using realistic gravitational-wave detector data in machine learning approaches, and represent a step towards achieving real-time detection and inference of gravitational waves. |
| first_indexed | 2024-12-17T20:38:50Z |
| format | Article |
| id | doaj.art-1b56bbf990f04acea29abf48e485a84a |
| institution | Directory Open Access Journal |
| issn | 0370-2693 |
| language | English |
| last_indexed | 2024-12-17T20:38:50Z |
| publishDate | 2021-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Physics Letters B |
| spelling | doaj.art-1b56bbf990f04acea29abf48e485a84a2022-12-21T21:33:21ZengElsevierPhysics Letters B0370-26932021-04-01815136161Detection and parameter estimation of gravitational waves from binary neutron-star mergers in real LIGO data using deep learningPlamen G. Krastev0Kiranjyot Gill1V. Ashley Villar2Edo Berger3Harvard University, Faculty of Arts and Sciences, Research Computing, 38 Oxford Street, Cambridge, MA 02138, USA; Corresponding author.Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138, USAHarvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138, USA; Simons Junior Fellow, Department of Astronomy, Columbia University, New York, NY 10027, USAHarvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138, USAOne of the key challenges of real-time detection and parameter estimation of gravitational waves from compact binary mergers is the computational cost of conventional matched-filtering and Bayesian inference approaches. In particular, the application of these methods to the full signal parameter space available to the gravitational-wave detectors, and/or real-time parameter estimation is computationally prohibitive. On the other hand, rapid detection and inference are critical for prompt follow-up of the electromagnetic and astro-particle counterparts accompanying important transients, such as binary neutron-star and black-hole neutron-star mergers. Training deep neural networks to identify specific signals and learn a computationally efficient representation of the mapping between gravitational-wave signals and their parameters allows both detection and inference to be done quickly and reliably, with high sensitivity and accuracy. In this work we apply a deep-learning approach to rapidly identify and characterize transient gravitational-wave signals from binary neutron-star mergers in real LIGO data. We show for the first time that artificial neural networks can promptly detect and characterize binary neutron star gravitational-wave signals in real LIGO data, and distinguish them from noise and signals from coalescing black-hole binaries. We illustrate this key result by demonstrating that our deep-learning framework classifies correctly all gravitational-wave events from the Gravitational-Wave Transient Catalog, GWTC-1 [Abbott et al. (2019) [4]]. These results emphasize the importance of using realistic gravitational-wave detector data in machine learning approaches, and represent a step towards achieving real-time detection and inference of gravitational waves.http://www.sciencedirect.com/science/article/pii/S0370269321001015 |
| spellingShingle | Plamen G. Krastev Kiranjyot Gill V. Ashley Villar Edo Berger Detection and parameter estimation of gravitational waves from binary neutron-star mergers in real LIGO data using deep learning Physics Letters B |
| title | Detection and parameter estimation of gravitational waves from binary neutron-star mergers in real LIGO data using deep learning |
| title_full | Detection and parameter estimation of gravitational waves from binary neutron-star mergers in real LIGO data using deep learning |
| title_fullStr | Detection and parameter estimation of gravitational waves from binary neutron-star mergers in real LIGO data using deep learning |
| title_full_unstemmed | Detection and parameter estimation of gravitational waves from binary neutron-star mergers in real LIGO data using deep learning |
| title_short | Detection and parameter estimation of gravitational waves from binary neutron-star mergers in real LIGO data using deep learning |
| title_sort | detection and parameter estimation of gravitational waves from binary neutron star mergers in real ligo data using deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S0370269321001015 |
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