Extraction of Binary Black Hole Gravitational Wave Signals from Detector Data Using Deep Learning

TL;DR

This paper presents a deep learning approach using CNN and LSTM to rapidly extract binary black hole gravitational wave signals from noisy detector data, achieving high accuracy and robustness in real-time scenarios.

Contribution

The authors develop a deep learning model capable of fast, accurate GW waveform extraction from realistic noise, outperforming traditional methods in speed and robustness.

Findings

Achieves over 0.95 waveform overlap for SNR > 6

Attains over 0.97 overlap for all ten detected BBH events

Operates in a few milliseconds, enabling real-time analysis

Abstract

Accurate extractions of the detected gravitational wave (GW) signal waveforms are essential to validate a detection and to probe the astrophysics behind the sources producing the GWs. This however could be difficult in realistic scenarios where the signals detected by existing GW detectors could be contaminated with non-stationary and non-Gaussian noise. While the performance of existing waveform extraction methods are optimal, they are not fast enough for online application, which is important for multi-messenger astronomy. In this paper, we demonstrate that a deep learning architecture consisting of Convolutional Neural Network and bidirectional Long Short-Term Memory components can be used to extract binary black hole (BBH) GW waveforms from realistic noise in a few milli-seconds. We have tested our network systematically on injected GW signals, with component masses uniformly distributed in the range of 10 to 80 solar masses, on Gaussian noise and LIGO detector noise. We find that our model can extract GW waveforms with overlaps of more than 0.95 with pure Numerical Relativity templates for signals with signal-to-noise ratio (SNR) greater than six, and is also robust against interfering glitches. We then apply our model to all ten detected BBH events from the first (O1) and second (O2) observation runs, obtaining greater than 0.97 overlaps for all ten extracted BBH waveforms with the corresponding pure templates. We discuss the implication of our result and its future applications to GW localization and mass estimation.