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Deep learning for gravitational wave forecasting of neutron star mergers

Wei Wei
(
- NCSA, Urbana and
- Illinois U., Urbana
)
,
E.A. Huerta
(
)

Oct 19, 2020

10 pages

Published in:

Phys.Lett.B 816 (2021) 136185

Published: May 10, 2021

e-Print:

2010.09751 [gr-qc]

DOI:

10.1016/j.physletb.2021.136185 (publication)

View in:

reference search44 citations

Citations per year

Abstract: (arXiv)

We introduce deep learning time-series forecasting for gravitational wave detection of binary neutron star mergers. This method enables the identification of these signals in real advanced LIGO data up to 30 seconds before merger. When applied to GW170817, our deep learning forecasting method identifies the presence of this gravitational wave signal 10 seconds before merger. This novel approach requires a single GPU for inference, and may be used as part of an early warning system for time-sensitive multi-messenger searches.

Note:

10 pages, 4 figures

Gravitational waves
Deep learning
Prediction
Neutron stars
LIGO
neutron star: binary
gravitational radiation
binary: coalescence
gravitational radiation: emission
gravitational radiation detector

References(26)

Figures(12)

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