Deep learning model on gravitational waveforms in merging and ringdown phases of binary black hole coalescences

The waveform templates of the matched filtering-based gravitational-wave search ought to cover wide range of parameters for the prosperous detection. Numerical relativity (NR) has been widely accepted as the most accurate method for modeling the waveforms. Still, it is well known that NR typically r...

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Bibliographic Details
Published in:Physical review. D 2021-06, Vol.103 (12), p.1, Article 123023
Main Authors: Lee, Joongoo, Oh, Sang Hoon, Kim, Kyungmin, Cho, Gihyuk, Oh, John J., Son, Edwin J., Lee, Hyung Mok
Format: Article
Language:English
Subjects:
Black holes
Coalescing
Deep learning
Filtration
Gravitational waves
Numerical relativity
Relativity
Template matching
Waveforms
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Summary:The waveform templates of the matched filtering-based gravitational-wave search ought to cover wide range of parameters for the prosperous detection. Numerical relativity (NR) has been widely accepted as the most accurate method for modeling the waveforms. Still, it is well known that NR typically requires a tremendous amount of computational costs. In this paper, we demonstrate a proof-of-concept of a novel deterministic deep learning (DL) architecture that can generate gravitational waveforms from the merger and ringdown phases of the non-spinning binary black hole coalescence. Our model takes O(1) seconds for generating approximately 1500 waveforms with a 99.9% match on average to one of the state-of-the-art waveform approximants, the effective-one-body. We also perform matched filtering with the DL-waveforms and find that the waveforms can recover the event time of the injected gravitational-wave signals.
ISSN:2470-0010
2470-0029
DOI:10.1103/PhysRevD.103.123023