Reconstruction of binary black hole harmonics in LIGO using deep learning

Abstract

Gravitational wave signals from coalescing compact binaries in the LIGO and Virgo interferometers are primarily detected by the template based matched filtering method. While this method is optimal for stationary and Gaussian data scenarios, its sensitivity is often affected by non stationary noise transients in the detectors. Moreover, most of the current searches do not account for the effects of precession of black hole spins and higher order waveform harmonics, focusing solely on the leading order quadrupolar modes. This limitation impacts our search for interesting astrophysical sources, such as intermediate mass black hole binaries and hierarchical mergers. Here we show for the first time that deep learning can be used for accurate waveform reconstruction of precessing binary black hole signals with higher order modes. This approach can also be adapted into a rapid trigger generation algorithm to enhance online searches. Our model, tested on simulated injections in real LIGO noise from the third observing run achieved high-degree of overlap with injected signals. This accuracy was consistent across a wide range of black hole masses and spin configurations chosen for this study. When applied to real gravitational wave events, our reconstructions achieved between 0.85 and 0.98 overlaps with those obtained by Coherent WaveBurst (unmodeled) and LALInference (modeled) analyses. These results suggest that deep learning is a potent tool for analyzing signals from a diverse catalog of compact binaries.

References in corpus (56)

• Observation of Gravitational Waves from a Binary Black Hole Merger
• Advanced Virgo: a 2nd generation interferometric gravitational wave detector
• Advanced LIGO
• GW151226: Observation of Gravitational Waves from a 22-Solar-Mass Binary Black Hole Coalescence
• GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run
• GW190521: A Binary Black Hole Merger with a Total Mass of $150M_{\odot }$
• GW190412: Observation of a Binary-Black-Hole Coalescence with Asymmetric Masses
• The PyCBC search for gravitational waves from compact binary coalescence
• Inspiral-merger-ringdown waveforms for black-hole binaries with non-precessing spins
• Computationally efficient models for the dominant and sub-dominant harmonic modes of precessing binary black holes
• KAGRA: 2.5 Generation Interferometric Gravitational Wave Detector
• Matched filtering of gravitational waves from inspiraling compact binaries: Computational cost and template placement
• Inspiral, merger and ringdown of unequal mass black hole binaries: a multipolar analysis
• Towards models of gravitational waveforms from generic binaries II: Modelling precession effects with a single effective precession parameter
• Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data
• Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science
• Open data from the third observing run of LIGO, Virgo, KAGRA and GEO
• Multipolar Effective-One-Body Waveforms for Precessing Binary Black Holes: Construction and Validation
• Deep Neural Networks to Enable Real-time Multimessenger Astrophysics
• Matching matched filtering with deep networks in gravitational-wave astronomy
• The third post-Newtonian gravitational wave polarisations and associated spherical harmonic modes for inspiralling compact binaries in quasi-circular orbits
• Modeling Ringdown: Beyond the Fundamental Quasi-Normal Modes
• Phenomenological model for the gravitational-wave signal from precessing binary black holes with two-spin effects
• Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy
• Glitch Classification and Clustering for LIGO with Deep Transfer Learning
• Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO
• Enhancing Gravitational-Wave Science with Machine Learning
• Searching for Gravitational Waves from Compact Binaries with Precessing Spins
• Convolutional neural networks: a magic bullet for gravitational-wave detection?
• Nested Sampling with Normalising Flows for Gravitational-Wave Inference
• Learning Bayesian posteriors with neural networks for gravitational-wave inference
• Machine-learning non-stationary noise out of gravitational wave detectors
• Real-Time Detection of Gravitational Waves from Binary Neutron Stars using Artificial Neural Networks
• Gravitational-wave observations of binary black holes: Effect of non-quadrupole modes
• Gravitational Wave Denoising of Binary Black Hole Mergers with Deep Learning
• Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference
• Impact of Higher Harmonics in Searching for Gravitational Waves from Non-Spinning Binary Black Holes
• Detectability of Intermediate-Mass Black Holes in Multiband Gravitational Wave Astronomy
• Impact of gravitational radiation higher order modes on single aligned-spin gravitational wave searches for binary black holes
• Detectability of gravitational waves from binary black holes: Impact of precession and higher modes
• A multimode quasi-normal spectrum from a perturbed black hole
• Searching for the full symphony of black hole binary mergers
• Aperture synthesis for gravitational-wave data analysis: Deterministic Sources
• Classification methods for noise transients in advanced gravitational-wave detectors II: performance tests on Advanced LIGO data
• MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge
• Tracking black hole kicks from gravitational wave observations
• Impact of Higher-order Modes on the Detection of Binary Black Hole Coalescences
• Sensitivity of gravitational wave searches to the full signal of intermediate mass black hole binaries during the LIGO O1 Science Run
• Deep Learning Detection and Classification of Gravitational Waves from Neutron Star-Black Hole Mergers
• First gravitational-wave search for intermediate-mass black hole mergers with higher order harmonics
• Total-variation methods for gravitational-wave denoising: performance tests on Advanced LIGO data
• Machine Learning on Difference Image Analysis: A comparison of methods for transient detection
• Generating transient noise artifacts in gravitational-wave detector data with generative adversarial networks
• Rapid localization of gravitational wave sources from compact binary coalescences using deep learning
• Pre-merger sky localization of gravitational waves from binary neutron star mergers using deep learning
• Post-merger chirps from binary black holes as probes of the final black-hole horizon

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• Sky localization and polarization mode reconstruction of gravitational waves from GW170104 and GW150914