Detection and Parameter Estimation of Gravitational Waves from Binary Neutron-Star Mergers in Real LIGO Data using Deep Learning

Abstract

One of the key challenges of real-time detection and parameter estimation of gravitational waves from compact binary mergers is the computational cost of conventional matched-filtering and Bayesian inference approaches. In particular, the application of these methods to the full signal parameter space available to the gravitational-wave detectors, and/or real-time parameter estimation is computationally prohibitive. On the other hand, rapid detection and inference are critical for prompt follow-up of the electromagnetic and astro-particle counterparts accompanying important transients, such as binary neutron-star and black-hole neutron-star mergers. Training deep neural networks to identify specific signals and learn a computationally efficient representation of the mapping between gravitational-wave signals and their parameters allows both detection and inference to be done quickly and reliably, with high sensitivity and accuracy. In this work we apply a deep-learning approach to rapidly identify and characterize transient gravitational-wave signals from binary neutron-star mergers in real LIGO data. We show for the first time that artificial neural networks can promptly detect and characterize binary neutron star gravitational-wave signals in real LIGO data, and distinguish them from noise and signals from coalescing black-hole binaries. We illustrate this key result by demonstrating that our deep-learning framework classifies correctly all gravitational-wave events from the Gravitational-Wave Transient Catalog, GWTC-1 [Phys. Rev. X 9 (2019), 031040]. These results emphasize the importance of using realistic gravitational-wave detector data in machine learning approaches, and represent a step towards achieving real-time detection and inference of gravitational waves.

arXiv comment10 pages, 8 figures, 1 table. Updated grant information. Published in Physics Letters B

References in corpus (39)

• Adam: A Method for Stochastic Optimization
• Observation of Gravitational Waves from a Binary Black Hole Merger
• GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral
• Multi-messenger Observations of a Binary Neutron Star Merger
• The equation of state for nucleon matter and neutron star structure
• GWTC-2: Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run
• GW190814: Gravitational Waves from the Coalescence of a 23 M${}_{\odot }$ Black Hole with a 2.6 M${}_{\odot }$ Compact Object
• Tests of general relativity with GW150914
• GW190425: Observation of a Compact Binary Coalescence with Total Mass $\sim 3.4M_{\odot }$
• On the Convergence of Adam and Beyond
• GW190521: A Binary Black Hole Merger with a Total Mass of $150M_{\odot }$
• Recent Trends in Deep Learning Based Natural Language Processing
• Tidal deformability of neutron stars with realistic equations of state and their gravitational wave signatures in binary inspiral
• Astrophysical Implications of the Binary Black-Hole Merger GW150914
• A simple model of complete precessing black-hole-binary gravitational waveforms
• GW190412: Observation of a Binary-Black-Hole Coalescence with Asymmetric Masses
• 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
• Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data
• Inspiral-merger-ringdown waveforms of spinning, precessing black-hole binaries in the effective-one-body formalism
• Deep Neural Networks to Enable Real-time Multimessenger Astrophysics
• Matching matched filtering with deep networks in gravitational-wave astronomy
• Implementing a search for aligned-spin neutron star -- black hole systems with advanced ground based gravitational wave detectors
• Fast and accurate prediction of numerical relativity waveforms from binary black hole coalescences using surrogate models
• Frequency domain reduced order model of aligned-spin effective-one-body waveforms with generic mass-ratios and spins
• Searching for Gravitational Waves from Compact Binaries with Precessing Spins
• Convolutional neural networks: a magic bullet for gravitational-wave detection?
• Learning Bayesian posteriors with neural networks for gravitational-wave inference
• Uncertainties in Parameters Estimated with Neural Networks: Application to Strong Gravitational Lensing
• Complete waveform model for compact binaries on eccentric orbits
• Real-Time Detection of Gravitational Waves from Binary Neutron Stars using Artificial Neural Networks
• Gravitational Wave Denoising of Binary Black Hole Mergers with Deep Learning
• Gravitational wave signal recognition of O1 data by deep learning
• Enabling real-time multi-messenger astrophysics discoveries with deep learning
• Imprints of the nuclear symmetry energy on the tidal deformability of neutron stars
• Deep Learning Ensemble for Real-time Gravitational Wave Detection of Spinning Binary Black Hole Mergers
• Detection of Gravitational Waves Using Bayesian Neural Networks
• Improved deep learning techniques in gravitational-wave data analysis
• Quasi-5.5PN TaylorF2 approximant for compact binaries: point-mass phasing and impact on the tidal polarizability inference

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• Rapid Parameter Estimation for Merging Massive Black Hole Binaries Using Continuous Normalizing Flows
• Search for exotic gravitational wave signals beyond general relativity using deep learning
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