Rapid identification of time-frequency domain gravitational wave signals from binary black holes using deep learning

Abstract

Recent developments in deep learning techniques have offered an alternative and complementary approach to traditional matched filtering methods for the identification of gravitational wave (GW) signals. The rapid and accurate identification of GW signals is crucial for the progress of GW physics and multi-messenger astronomy, particularly in light of the upcoming fourth and fifth observing runs of LIGO-Virgo-KAGRA. In this work, we use the 2D U-Net algorithm to identify the time-frequency domain GW signals from stellar-mass binary black hole (BBH) mergers. We simulate BBH mergers with component masses from 5 to 80 $M_{\odot }$ and account for the LIGO detector noise. We find that the GW events in the first and second observation runs could all be clearly and rapidly identified. For the third observing run, about $80\%$ GW events could be identified. In particular, GW190814, currently unknown, is a special case that can be identified by the network, while other binary neutron star mergers and neutron star-black hole mergers can not be identified. Compared to the traditional convolutional neural network, the U-Net algorithm can output the time-frequency domain signal images rather than probabilities, providing a more intuitive investigation. Moreover, some of the results through U-Net can provide preliminary inference on the chirp mass information. In conclusion, the U-Net algorithm can rapidly identify the time-frequency domain GW signals from BBH mergers and potentially be helpful for future parameter inferences.

15 pages, 11 figures

References in corpus (41)

• GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral
• Advanced Virgo: a 2nd generation interferometric gravitational wave detector
• Multi-messenger Observations of a Binary Neutron Star Merger
• Advanced LIGO
• A gravitational-wave standard siren measurement of the Hubble constant
• First measurement of the Hubble constant from a dark standard siren using the Dark Energy Survey galaxies and the LIGO/Virgo binary-black-hole merger GW170814
• Determination of Dark Energy by the Einstein Telescope: Comparing with CMB, BAO and SNIa Observations
• Estimating cosmological parameters by the simulated data of gravitational waves from the Einstein Telescope
• Search for gravitational waves from binary inspirals in S3 and S4 LIGO data
• Spectral sirens: cosmology from the full mass distribution of compact binaries
• Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference
• The Gravitational-Wave Physics II: Progress
• Beyond Concordance Cosmology with Magnification of Gravitational-Wave Standard Sirens
• A Program for Multi-Messenger Standard Siren Cosmology in the Era of LIGO A+, Rubin Observatory, and Beyond
• MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge
• First demonstration of early warning gravitational wave alerts
• Deep Learning Ensemble for Real-time Gravitational Wave Detection of Spinning Binary Black Hole Mergers
• Deep learning and Bayesian inference of gravitational-wave populations: Hierarchical black-hole mergers
• The Taiji-TianQin-LISA network: Precisely measuring the Hubble constant using both bright and dark sirens
• Synergy between CSST galaxy survey and gravitational-wave observation: Inferring the Hubble constant from dark standard sirens
• Deep Residual Networks for Gravitational Wave Detection
• Prospects for measuring the Hubble constant and dark energy using gravitational-wave dark sirens with neutron star tidal deformation
• Impacts of gravitational-wave standard siren observations from Einstein Telescope and Cosmic Explorer on weighing neutrinos in interacting dark energy models
• Normalizing flows as an avenue to study overlapping gravitational wave signals
• Using Deep Learning to Localize Gravitational Wave Sources
• Prospects for probing the interaction between dark energy and dark matter using gravitational-wave dark sirens with neutron star tidal deformation
• Improved deep learning techniques in gravitational-wave data analysis
• Deep Learning Detection and Classification of Gravitational Waves from Neutron Star-Black Hole Mergers
• Eliminating Primary Beam Effect in Foreground Subtraction of Neutral Hydrogen Intensity Mapping Survey with Deep Learning
• Joint constraints on cosmological parameters using future multi-band gravitational wave standard siren observations
• Ensemble of Deep Convolutional Neural Networks for real-time gravitational wave signal recognition
• An accurate method to determine the systematics due to the peculiar velocities of galaxies in measuring the Hubble constant from gravitational wave standard sirens
• A comprehensive forecast for cosmological parameter estimation using joint observations of gravitational waves and short $\gamma$-ray bursts
• Efficient parameter inference for gravitational wave signals in the presence of transient noises using temporal and time-spectral fusion normalizing flow
• Rapid localization of gravitational wave sources from compact binary coalescences using deep learning
• Searching for modified gravity in the astrophysical gravitational wave background: Application to ground-based interferometers
• Standard siren cosmology in the era of the 2.5-generation ground-based gravitational wave detectors: bright and dark sirens of LIGO Voyager and NEMO
• Eliminating polarization leakage effect for neutral hydrogen intensity mapping with deep learning
• Gravitational wave constraints on non-birefringent dispersions of gravitational waves due to Lorentz violations with GWTC-3
• Standard sirens and dark sector with Gaussian process
• Neural network time-series classifiers for gravitational-wave searches in single-detector periods

Cited by in corpus (6)

• Robust inference of gravitational wave source parameters in the presence of noise transients using normalizing flows
• Novel Deep Learning Approach to Detecting Binary Black Hole Mergers
• Search for exotic gravitational wave signals beyond general relativity using deep learning
• Prospects for joint multiband detection of intermediate-mass black holes by LGWA and the Einstein Telescope
• Denoising gravitational wave with deep learning in the time-frequency domain
• Parameter inference of millilensed gravitational waves using neural spline flows