Deep Learning with Quantized Neural Networks for Gravitational-wave Forecasting of Eccentric Compact Binary Coalescence
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- Univ. of Illinois at Urbana-Champaign, IL (United States)
- Univ. of Illinois at Urbana-Champaign, IL (United States); Argonne National Lab. (ANL), Argonne, IL (United States); Univ. of Chicago, IL (United States)
- Princeton Univ., NJ (United States)
- Tata Inst. of Fundamental Research, Bangalore (India)
- Tata Inst. of Fundamental Research, Bangalore (India); Cornell Univ., Ithaca, NY (United States)
We present the first application of deep learning forecasting for binary neutron stars, neutron star–black hole systems, and binary black hole mergers that span an eccentricity range e ≤ 0.9. In this work, 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 4× reduction in model size, and up to 2.5× inference speedup. These novel algorithms may be used to facilitate time-sensitive multimessenger astrophysics observations of compact binaries in dense stellar environments.
- Research Organization:
- Argonne National Laboratory (ANL), Argonne, IL (United States)
- Sponsoring Organization:
- National Science Foundation (NSF); NVIDIA; Simons Foundation; Canadian Institute for Advanced Research (CIFAR); USDOE
- Grant/Contract Number:
- AC02-06CH11357
- OSTI ID:
- 1844212
- Journal Information:
- The Astrophysical Journal, Journal Name: The Astrophysical Journal Journal Issue: 2 Vol. 919; ISSN 0004-637X
- Publisher:
- IOP PublishingCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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