First neural-network subgrid model for special relativistic MHD reproduces 4x-higher-resolution magnetic field amplification in 3D Kelvin-Helmholtz tests at 44x speedup.
Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data
5 Pith papers cite this work, alongside 379 external citations. Polarity classification is still indexing.
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abstract
The recent Nobel-prize-winning detections of gravitational waves from merging black holes and the subsequent detection of the collision of two neutron stars in coincidence with electromagnetic observations have inaugurated a new era of multimessenger astrophysics. To enhance the scope of this emergent field of science, we pioneered the use of deep learning with convolutional neural networks, that take time-series inputs, for rapid detection and characterization of gravitational wave signals. This approach, Deep Filtering, was initially demonstrated using simulated LIGO noise. In this article, we present the extension of Deep Filtering using real data from LIGO, for both detection and parameter estimation of gravitational waves from binary black hole mergers using continuous data streams from multiple LIGO detectors. We demonstrate for the first time that machine learning can detect and estimate the true parameters of real events observed by LIGO. Our results show that Deep Filtering achieves similar sensitivities and lower errors compared to matched-filtering while being far more computationally efficient and more resilient to glitches, allowing real-time processing of weak time-series signals in non-stationary non-Gaussian noise with minimal resources, and also enables the detection of new classes of gravitational wave sources that may go unnoticed with existing detection algorithms. This unified framework for data analysis is ideally suited to enable coincident detection campaigns of gravitational waves and their multimessenger counterparts in real-time.
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A contrastive self-supervised convolutional autoencoder detects core-collapse supernova gravitational waves with performance comparable to supervised CNNs, better generalization to unseen waveforms, and ~120 kpc sensitive distance under Einstein Telescope noise.
A novel quantity derived from GW signals encodes the density profile of dark dense environments around black holes, allowing characterization of the condensate type and DM properties via multi-wavelength observations.
Deep learning models on simulated ET and CE data achieve higher accuracy (0.655–0.897) than matched filtering (0.514–0.689) at classifying g-mode resonances versus adiabatic tides, point particles, and noise in eccentric binary neutron star signals.
BILBY is validated on simulated compact binary signals and reproduces the eleven GWTC-1 results with configuration and output files provided for reproduction.
citing papers explorer
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Subgrid Modelling for Relativistic Magnetohydrodynamics with Machine Learning
First neural-network subgrid model for special relativistic MHD reproduces 4x-higher-resolution magnetic field amplification in 3D Kelvin-Helmholtz tests at 44x speedup.
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Contrastive self-supervised convolutional autoencoder for core-collapse supernova gravitational-wave detection
A contrastive self-supervised convolutional autoencoder detects core-collapse supernova gravitational waves with performance comparable to supervised CNNs, better generalization to unseen waveforms, and ~120 kpc sensitive distance under Einstein Telescope noise.
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Extracting Properties of Dark Dense Environments around Black Holes from Gravitational Waves
A novel quantity derived from GW signals encodes the density profile of dark dense environments around black holes, allowing characterization of the condensate type and DM properties via multi-wavelength observations.
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Identifiability of $g$ mode Resonances in Eccentric Binary Neutron Stars with Multidetector Observations
Deep learning models on simulated ET and CE data achieve higher accuracy (0.655–0.897) than matched filtering (0.514–0.689) at classifying g-mode resonances versus adiabatic tides, point particles, and noise in eccentric binary neutron star signals.
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Bayesian inference for compact binary coalescences with BILBY: Validation and application to the first LIGO--Virgo gravitational-wave transient catalogue
BILBY is validated on simulated compact binary signals and reproduces the eleven GWTC-1 results with configuration and output files provided for reproduction.