Rapid localization of gravitational wave sources from compact binary coalescences using deep learning

Abstract

The mergers of neutron star-neutron star and neutron star-black hole binaries are the most promising gravitational wave events with electromagnetic counterparts. The rapid detection, localization and simultaneous multi-messenger follow-up of these sources is of primary importance in the upcoming science runs of the LIGO-Virgo-KAGRA Collaboration. While prompt electromagnetic counterparts during binary mergers can last less than two seconds, the time scales of existing localization methods that use Bayesian techniques, varies from seconds to days. In this paper, we propose the first deep learning-based approach for rapid and accurate sky localization of all types of binary coalescences, including neutron star-neutron star and neutron star-black hole binaries for the first time. Specifically, we train and test a normalizing flow model on matched-filtering output from gravitational wave searches. Our model produces sky direction posteriors in milliseconds using a single P100 GPU, which is three to six orders of magnitude faster than Bayesian techniques.

18 pages, 8 figures

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Cited by in corpus (11)

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• Binary Neutron Star Merger Search Pipeline Powered by Deep Learning
• Reconstruction of binary black hole harmonics in LIGO using deep learning
• Novel Deep Learning Approach to Detecting Binary Black Hole Mergers
• Sky localization of gravitational waves from eccentric binaries
• Comparing next-generation detector configurations for high-redshift gravitational wave sources with neural posterior estimation
• Flexible Gravitational-Wave Parameter Estimation with Transformers
• Extract non-Gaussian Features in Gravitational Wave Observation Data Using Self-Supervised Learning
• Sky localization and polarization mode reconstruction of gravitational waves from GW170104 and GW150914
• Binary Black Hole Parameter Estimation with Hybrid CNN-Transformer Neural Networks