Deep Learning with Quantized Neural Networks for Gravitational Wave Forecasting of Eccentric Compact Binary Coalescence

TL;DR

This paper introduces deep learning methods, including quantized neural networks, for early detection and parameter estimation of eccentric binary mergers in gravitational wave data, enhancing speed and efficiency.

Contribution

It presents the first application of deep learning for forecasting eccentric binary mergers, with quantized neural networks reducing model size and increasing inference speed.

Findings

Deep learning detects signals seconds before merger.

Quantized networks reduce model size by 4x.

Inference speed increases by up to 2.5x.

Abstract

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. We train neural networks that describe these astrophysical populations, and then test their performance by injecting simulated eccentric signals in advanced LIGO noise available at the \texttt{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 4x reduction in model size, and up to 2.5x inference speed up. These novel algorithms may be used to facilitate time-sensitive multi-messenger astrophysics observations of compact binaries in dense stellar environments.