Premerger detection of massive black hole binaries using deep learning

Abstract

Coalescing massive black hole binaries (MBHBs) are one of primary sources for space-based gravitational wave (GW) observations. The mergers of these binaries are expected to give rise to detectable electromagnetic (EM) emissions with a narrow time window. The premerger detection of GW signals is vital for follow-up EM observations. The conventional approach for searching GW signals involves high computational costs. In this study, we present a deep learning model to search for GW signals from MBHBs. Our model is able to process 4.7 days of simulated data within 0.01 seconds and detect GW signals several hours to days before the final merger. The model provides the possibility of the coincident GW and EM detection of MBHBs.

arXiv comment9 pages, 7 figures

References in corpus (62)

• The Demography of Massive Dark Objects in Galaxy Centres
• GWTC-1: A Gravitational-Wave Transient Catalog of Compact Binary Mergers Observed by LIGO and Virgo during the First and Second Observing Runs
• GW151226: Observation of Gravitational Waves from a 22-Solar-Mass Binary Black Hole Coalescence
• GW170104: Observation of a 50-Solar-Mass Binary Black Hole Coalescence at Redshift 0.2
• GWTC-2: Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run
• TianQin: a space-borne gravitational wave detector
• GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run
• The NANOGrav 15-year Data Set: Evidence for a Gravitational-Wave Background
• Search for an isotropic gravitational-wave background with the Parkes Pulsar Timing Array
• Frequency-domain gravitational waves from non-precessing black-hole binaries. II. A phenomenological model for the advanced detector era
• On gravitational-wave spectroscopy of massive black holes with the space interferometer LISA
• Frequency-domain gravitational waves from non-precessing black-hole binaries. I. New numerical waveforms and anatomy of the signal
• Using gravitational-wave standard sirens
• The NANOGrav 12.5-year Data Set: Search For An Isotropic Stochastic Gravitational-Wave Background
• The runaway growth of intermediate-mass black holes in dense star clusters
• Intermediate and Extreme Mass-Ratio Inspirals -- Astrophysics, Science Applications and Detection using LISA
• Taiji Program: Gravitational-Wave Sources
• GW150914: First results from the search for binary black hole coalescence with Advanced LIGO
• The gravitational wave signal from the Galactic disk population of binaries containing two compact objects
• Matched filtering of gravitational waves from inspiraling compact binaries: Computational cost and template placement
• A 2 per cent Hubble constant measurement from standard sirens within 5 years
• Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data
• The LISA Optimal Sensitivity
• Deep Neural Networks to Enable Real-time Multimessenger Astrophysics
• Matching matched filtering with deep networks in gravitational-wave astronomy
• Dual Jets from Binary Black Holes
• Towards a formalism for mapping the spacetimes of massive compact objects: Bumpy black holes and their orbits
• Enhancing Gravitational-Wave Science with Machine Learning
• Convolutional neural networks: a magic bullet for gravitational-wave detection?
• Characterization of the stochastic signal originating from compact binaries populations as measured by LISA
• Electromagnetic Counterparts to Massive Black Hole Mergers
• Real-Time Detection of Gravitational Waves from Binary Neutron Stars using Artificial Neural Networks
• Deep-Learning continuous gravitational waves
• Gravitational wave signal recognition of O1 data by deep learning
• Mergers of Supermassive Black Holes in Astrophysical Environments
• Forecast for cosmological parameter estimation with gravitational-wave standard sirens from the LISA-Taiji network
• Detection and Classification of Supernova Gravitational Waves Signals: A Deep Learning Approach
• The Search for Massive Black Hole Binaries with LISA
• Detection of gravitational-wave signals from binary neutron star mergers using machine learning
• MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge
• Effectual gravitational-wave template banks for coalescing compact binaries using a hybrid placement algorithm
• Minidisk dynamics in accreting, spinning black hole binaries: Simulations in full general relativity
• The Taiji-TianQin-LISA network: Precisely measuring the Hubble constant using both bright and dark sirens
• Deep-Learning Continuous Gravitational Waves: Multiple detectors and realistic noise
• Catching Super Massive Black Hole Binaries Without a Net
• Training Strategies for Deep Learning Gravitational-Wave Searches
• Deep Residual Networks for Gravitational Wave Detection
• Improved deep learning techniques in gravitational-wave data analysis
• Space-based gravitational wave signal detection and extraction with deep neural network
• Ensemble of Deep Convolutional Neural Networks for real-time gravitational wave signal recognition
• LSTM and CNN application for core-collapse supernova search in gravitational wave real data
• Search for gravitational-wave bursts in the third Advanced LIGO-Virgo run with coherent WaveBurst enhanced by Machine Learning
• Extreme-mass-ratio burst detection with TianQin
• Detectability of modulated X-rays from LISA's supermassive black hole mergers
• Rapid search for massive black hole binary coalescences using deep learning
• Exploring gravitational-wave detection and parameter inference using Deep Learning methods
• Use of Excess Power Method and Convolutional Neural Network in All-Sky Search for Continuous Gravitational Waves
• Gravitational-Wave Searches for Cosmic String Cusps in Einstein Telescope Data using Deep Learning
• Dilated convolutional neural network for detecting extreme-mass-ratio inspirals
• SiGMa-Net: Deep learning network to distinguish binary black hole signals from short-duration noise transients
• Deep learning for intermittent gravitational wave signals
• Novel neural-network architecture for continuous gravitational waves

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• Gravitational Wave Astronomy With TianQin
• Applications of machine learning in gravitational wave research with current interferometric detectors
• A GPU-accelerated semi-coherent hierarchical search for stellar-mass binary inspiral signals in LISA
• Fast detection and reconstruction of merging Massive Black Hole Binary signals
• Accurate and efficient simulation-based inference for massive black-hole binaries with LISA