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

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.

arXiv comment14 pages, 13 figures

References in corpus (32)

• GWTC-1: A Gravitational-Wave Transient Catalog of Compact Binary Mergers Observed by LIGO and Virgo during the First and Second Observing Runs
• GWTC-2: Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run
• Robust parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library
• An improved effective-one-body model of spinning, nonprecessing binary black holes for the era of gravitational-wave astrophysics with advanced detectors
• Open data from the first and second observing runs of Advanced LIGO and Advanced Virgo
• BayesWave: Bayesian Inference for Gravitational Wave Bursts and Instrument Glitches
• Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science
• Deep Neural Networks to Enable Real-time Multimessenger Astrophysics
• Identifying Elusive Electromagnetic Counterparts to Gravitational Wave Mergers: an end-to-end simulation
• Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO
• Glitch Classification and Clustering for LIGO with Deep Transfer Learning
• Enhancing Gravitational-Wave Science with Machine Learning
• Gravitational-wave parameter estimation with autoregressive neural network flows
• Convolutional neural networks: a magic bullet for gravitational-wave detection?
• Learning Bayesian posteriors with neural networks for gravitational-wave inference
• Gravitational Wave Denoising of Binary Black Hole Mergers with Deep Learning
• Image-based deep learning for classification of noise transients in gravitational wave detectors
• Classification methods for noise transients in advanced gravitational-wave detectors
• Detection and Classification of Supernova Gravitational Waves Signals: A Deep Learning Approach
• Finding the origin of noise transients in LIGO data with machine learning
• Applying deep neural networks to the detection and space parameter estimation of compact binary coalescence with a network of gravitational wave detectors
• Deep learning for multimessenger core-collapse supernova detection
• Simulations of inspiraling and merging double neutron stars using the Spectral Einstein Code
• Some Optimizations on Detecting Gravitational Wave Using Convolutional Neural Network
• Complete phenomenological gravitational waveforms from spinning coalescing binaries
• Using Deep Learning to Localize Gravitational Wave Sources
• Classifying the Equation of State from Rotating Core Collapse Gravitational Waves with Deep Learning
• Total-variation methods for gravitational-wave denoising: performance tests on Advanced LIGO data
• Classification of gravitational-wave glitches via dictionary learning
• Application of Artificial Neural Network to Search for Gravitational-Wave Signals Associated with Short Gamma-Ray Bursts
• Predicting the properties of black holes merger remnants with Deep Neural Networks
• Sensitivity study using machine learning algorithms on simulated r-mode gravitational wave signals from newborn neutron stars

Cited by in corpus (4)

• Space-based gravitational wave signal detection and extraction with deep neural network
• Real-Time Detection of Unmodelled Gravitational-Wave Transients Using Convolutional Neural Networks
• Localization of gravitational waves using machine learning
• Deep learning for intermittent gravitational wave signals