Back to Search
Start Over
Deep Learning with Quantized Neural Networks for Gravitational-wave Forecasting of Eccentric Compact Binary Coalescence.
- Source :
-
Astrophysical Journal . 10/1/2021, Vol. 919 Issue 2, p1-10. 10p. - Publication Year :
- 2021
-
Abstract
- We present the first application of deep learning forecasting for binary neutron stars, neutron starâ€"black hole systems, and binary black hole mergers that span an eccentricity range e ≤ 0.9. We train neural networks that describe these astrophysical populations, and then test their performance by injecting simulated eccentric signals in advanced Laser Interferometer Gravitational-Wave Observatory (LIGO) noise available at the Gravitational Wave Open Science Center to (1) quantify how fast neural networks identify these signals before the binary components merge; (2) quantify how accurately neural networks estimate the time to merger once gravitational waves are identified; and (3) estimate the time-dependent sky localization of these events from early detection to merger. Our findings show that deep learning can identify eccentric signals from a few seconds (for binary black holes) up to tens of seconds (for binary neutron stars) prior to merger. A quantized version of our neural networks achieves 4Ă— reduction in model size, and up to 2.5Ă— inference speedup. These novel algorithms may be used to facilitate time-sensitive multimessenger astrophysics observations of compact binaries in dense stellar environments. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 0004637X
- Volume :
- 919
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- Astrophysical Journal
- Publication Type :
- Academic Journal
- Accession number :
- 152801072
- Full Text :
- https://doi.org/10.3847/1538-4357/ac1121