Detection of gravitational-wave signals from binary neutron star mergers using machine learning
| Autor: | Frank Ohme, Alexander H. Nitz, Marlin Schäfer |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Condensed matter Nuclear physics FOS: Physical sciences Binary number General Relativity and Quantum Cosmology (gr-qc) Machine learning computer.software_genre 01 natural sciences General Relativity and Quantum Cosmology Machine Learning (cs.LG) Fluid dynamics Search algorithm 0103 physical sciences General relativity (Physics) ddc:530 010306 general physics Instrumentation and Methods for Astrophysics (astro-ph.IM) Particles (Nuclear physics) High Energy Astrophysical Phenomena (astro-ph.HE) Physics 010308 nuclear & particles physics Gravitational wave business.industry Detector Quantum gravity LIGO Neutron star QUIET Dewey Decimal Classification::500 | Naturwissenschaften::530 | Physik Artificial intelligence Astrophysics - High Energy Astrophysical Phenomena Astrophysics - Instrumentation and Methods for Astrophysics business computer Merge (version control) Gravitation |
| Zdroj: | Physical Review D Physical Review D 102 (2020), Nr. 6 |
| Popis: | As two neutron stars merge, they emit gravitational waves that can potentially be detected by earth bound detectors. Matched-filtering based algorithms have traditionally been used to extract quiet signals embedded in noise. We introduce a novel neural-network based machine learning algorithm that uses time series strain data from gravitational-wave detectors to detect signals from non-spinning binary neutron star mergers. For the Advanced LIGO design sensitivity, our network has an average sensitive distance of 130 Mpc at a false-alarm rate of 10 per month. Compared to other state-of-the-art machine learning algorithms, we find an improvement by a factor of 6 in sensitivity to signals with signal-to-noise ratio below 25. However, this approach is not yet competitive with traditional matched-filtering based methods. A conservative estimate indicates that our algorithm introduces on average 10.2 s of latency between signal arrival and generating an alert. We give an exact description of our testing procedure, which can not only be applied to machine learning based algorithms but all other search algorithms as well. We thereby improve the ability to compare machine learning and classical searches. Comment: 14 pages, 6 figures, 1 table, supplemental materials at https://github.com/gwastro/bns-machine-learning-search |
| Databáze: | OpenAIRE |
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