Denoising gravitational wave with deep learning in the time-frequency domain

Abstract

Gravitational wave denoising is an ongoing task for revealing the events of compact binary objects in the universe. Recently, with the aid of deep learning, gravitational waves have been efficiently and delicately extracted from the noisy data compared with the traditional match-filtering. While most of the relevant studies adopt the data in the time series only, the time-frequency data processing is also in progress due to its several advantages for the waveform denoising. Here, we target the gravitational waves events emitted by binary black hole (BBH) mergers, with their total mass larger than 30 solar masses. For denoising, we propose a deep learning model utilizing the Griffin-Lim algorithm, an existing numerical approach to restore the phase information from the related amplitude spectrogram. This design allows extra attention on the phase recovery by using a priorly denoised amplitude spectrogram. The denoising results fit well in both the amplitude and the phase alignments of the mock injected waveforms. We also apply our model to the real detected events and discover a nice consistency with the simulated template waveforms, especially the high accuracy around the merger stage. Our work suggests the possibility of a better methodological design for gravitational wave data analysis.

References in corpus (39)

• Multi-messenger Observations of a Binary Neutron Star Merger
• Frequency-domain gravitational waves from non-precessing black-hole binaries. II. A phenomenological model for the advanced detector era
• Frequency-domain gravitational waves from non-precessing black-hole binaries. I. New numerical waveforms and anatomy of the signal
• Detection, Measurement and Gravitational Radiation
• FINDCHIRP: an algorithm for detection of gravitational waves from inspiraling compact binaries
• An improved effective-one-body model of spinning, nonprecessing binary black holes for the era of gravitational-wave astrophysics with advanced detectors
• Computationally efficient models for the dominant and sub-dominant harmonic modes of precessing binary black holes
• KAGRA: 2.5 Generation Interferometric Gravitational Wave Detector
• Method for detection and reconstruction of gravitational wave transients with networks of advanced detectors
• Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data
• 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
• IndIGO and LIGO-India: Scope and Plans for Gravitational Wave Research and Precision Metrology in India
• Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy
• Multi-Messenger Astrophysics
• Gravitational-wave parameter estimation with autoregressive neural network flows
• SEOBNRv5PHM: Next generation of accurate and efficient multipolar precessing-spin effective-one-body waveforms for binary black holes
• The BayesWave analysis pipeline in the era of gravitational wave observations
• Gravitational Wave Denoising of Binary Black Hole Mergers with Deep Learning
• Image-based deep learning for classification of noise transients in gravitational wave detectors
• The upgrade of GEO600
• Detection and Parameter Estimation of Gravitational Waves from Binary Neutron-Star Mergers in Real LIGO Data using Deep Learning
• Time-Frequency Analysis of Gravitational Wave Data
• Some Optimizations on Detecting Gravitational Wave Using Convolutional Neural Network
• Deep Residual Networks for Gravitational Wave Detection
• Space-based gravitational wave signal detection and extraction with deep neural network
• Deep Learning Detection and Classification of Gravitational Waves from Neutron Star-Black Hole Mergers
• Optimization of model independent gravitational wave search using machine learning
• Optimizing spinning time-domain gravitational waveforms for Advanced LIGO data analysis
• Ensemble of Deep Convolutional Neural Networks for real-time gravitational wave signal recognition
• An autoencoder neural network integrated into gravitational-wave burst searches to improve the rejection of noise transients
• Multi-messenger Approaches to Supermassive Black Hole Binary Detection and Parameter Estimation: Implications for Nanohertz Gravitational Wave Searches with Pulsar Timing Arrays
• Hierarchical approach to matched filtering using a reduced basis
• Comparative study of 1D and 2D convolutional neural network models with attribution analysis for gravitational wave detection from compact binary coalescences
• Rapid identification of time-frequency domain gravitational wave signals from binary black holes using deep learning
• Reducing the number of templates for aligned-spin compact binary coalescence gravitational wave searches using metric-agnostic template nudging
• Wavelet-based tools to analyze, filter, and reconstruct transient gravitational-wave signals
• DeepExtractor: Time-domain reconstruction of signals and glitches in gravitational wave data with deep learning