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Cited by 373Pulsars and Gravitational Waves ResearchSeismology and Earthquake StudiesGamma-ray bursts and supernovae

Deep learning for real-time gravitational wave detection and parameter estimation: results with advanced LIGO data

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George, Daniel, Huerta, E.A.·2018·Physics Letters B
DOI 10.1016/j.physletb.2017.12.053
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Abstract

The recent Nobel-prize-winning detections of gravitational waves from merging black holes and the subsequent detection of the collision of two neutron stars in coincidence with electromagnetic observations have inaugurated a new era of multimessenger astrophysics. To enhance the scope of this emergent field of science, we pioneered the use of deep learning with convolutional neural networks, that take time-series inputs, for rapid detection and characterization of gravitational wave signals. This approach, Deep Filtering, was initially demonstrated using simulated LIGO noise. In this article, we present the extension of Deep Filtering using real data from LIGO, for both detection and parameter estimation of gravitational waves from binary black hole mergers using continuous data streams from multiple LIGO detectors. We demonstrate for the first time that machine learning can detect and estimate the true parameters of real events observed by LIGO. Our results show that Deep Filtering achieves similar sensitivities and lower errors compared to matched-filtering while being far more computationally efficient and more resilient to glitches, allowing real-time processing of weak time-series signals in non-stationary non-Gaussian noise with minimal resources, and also enables the detection of new classes of gravitational wave sources that may go unnoticed with existing detection algorithms. This unified framework for data analysis is ideally suited to enable coincident detection campaigns of gravitational waves and their multimessenger counterparts in real-time.

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Authors
George, D., & Huerta, E. A.
Affiliations
University of Illinois Urbana-Champaign (US)
Corresponding author
George, Daniel
ORCID
Venue
Physics Letters B
Publisher
Elsevier BV
Published
2018-03
Volume
778
Pages
64-70
ISSN
0370-2693 (ISSN-L)
DOI
10.1016/j.physletb.2017.12.053
OpenAlex
W2767526854
Cited by
373
References
55
Citations by year
2026: 26 · 2025: 40 · 2024: 40 · 2023: 46 · 2022: 54 · 2021: 67 · 2020: 50 · 2019: 35 · 2018: 15
FWCI
15.26
Type
journal article
Language
en
Open access
Licence
Locations
  • Physics Letters B (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University)
  • Bulletin of the American Physical Society
Funders
National Science Foundation (OCI-0725070, ACI-1238993)
Topics
  • Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Pulsars and Gravitational Waves Research
  • Physical Sciences › Computer Science › Artificial Intelligence › Seismology and Earthquake Studies
  • Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Gamma-ray bursts and supernovae
Keywords
gravitational wave detection, convolutional neural networks, binary black hole mergers, parameter estimation, Advanced LIGO, real-time data processing, multi-messenger astronomy, gravitational wave sources

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