Denoising of gravitational wave signals via dictionary learning algorithms

Abstract

Gravitational wave astronomy has become a reality after the historical detections accomplished during the first observing run of the two advanced LIGO detectors. In the following years, the number of detections is expected to increase significantly with the full commissioning of the advanced LIGO, advanced Virgo and KAGRA detectors. The development of sophisticated data analysis techniques to improve the opportunities of detection for low signal-to-noise-ratio events is hence a most crucial effort. We present in this paper one such technique, dictionary-learning algorithms, which have been extensively developed in the last few years and successfully applied mostly in the context of image processing. However, to the best of our knowledge, such algorithms have not yet been employed to denoise gravitational wave signals. By building dictionaries from numerical relativity templates of both, binary black holes mergers and bursts of rotational core collapse, we show how machine-learning algorithms based on dictionaries can be also successfully applied for gravitational wave denoising. We use a subset of signals from both catalogs, embedded in non-white Gaussian noise, to assess our techniques with a large sample of tests and to find the best model parameters. The application of our method to the actual signal GW150914 shows promising results. Dictionary-learning algorithms could be a complementary addition to the gravitational wave data analysis toolkit. They may be used to extract signals from noise and to infer physical parameters if the data are in good enough agreement with the morphology of the dictionary atoms.

References in corpus (38)

• Observation of Gravitational Waves from a Binary Black Hole Merger
• GW151226: Observation of Gravitational Waves from a 22-Solar-Mass Binary Black Hole Coalescence
• Accurate Evolutions of Orbiting Black-Hole Binaries Without Excision
• LIGO: The Laser Interferometer Gravitational-Wave Observatory
• Binary Black Hole Mergers in the first Advanced LIGO Observing Run
• Interferometer design of the KAGRA gravitational wave detector
• Properties of the Binary Black Hole Merger GW150914
• Physics, Astrophysics and Cosmology with Gravitational Waves
• Transition from inspiral to plunge in binary black hole coalescences
• Task-Driven Dictionary Learning
• Gravitational radiation from cosmic (super)strings: bursts, stochastic background, and observational windows
• Gravitational-Radiation Damping of Compact Binary Systems to Second Post-Newtonian order
• The Sensitivity of the Advanced LIGO Detectors at the Beginning of Gravitational Wave Astronomy
• Matched filtering of gravitational waves from inspiraling compact binaries: Computational cost and template placement
• Method for detection and reconstruction of gravitational wave transients with networks of advanced detectors
• BayesWave: Bayesian Inference for Gravitational Wave Bursts and Instrument Glitches
• A catalog of 174 binary black-hole simulations for gravitational-wave astronomy
• Gravitational radiation from inspiralling compact binaries completed at the third post-Newtonian order
• Coherent method for detection of gravitational wave bursts
• The gravitational wave burst signal from core collapse of rotating stars
• Observing gravitational-wave transient GW150914 with minimal assumptions
• Gauge-invariant Non-spherical Metric Perturbations of Schwarzschild Black-Hole Spacetimes
• All-sky search for gravitational-wave bursts in the second joint LIGO-Virgo run
• Directly comparing GW150914 with numerical solutions of Einstein's equations for binary black hole coalescence
• Comparisons of binary black hole merger waveforms
• Gravitational-Wave Data Analysis. Formalism and Sample Applications: The Gaussian Case
• Gravitational wave signatures in black-hole-forming core collapse
• Bayesian reconstruction of gravitational wave burst signals from simulations of rotating stellar core collapse and bounce
• Measuring the Angular Momentum Distribution in Core-Collapse Supernova Progenitors with Gravitational Waves
• Classification methods for noise transients in advanced gravitational-wave detectors
• Inferring Core-Collapse Supernova Physics with Gravitational Waves
• Leveraging waveform complexity for confident detection of gravitational waves
• Enabling high confidence detections of gravitational-wave bursts
• Total-variation-based methods for gravitational wave denoising
• All-sky search for long-duration gravitational wave transients with LIGO
• Detection of gravitational waves from inspiraling compact binaries using a network of interferometric detectors
• Detecting gravitational-wave transients at five sigma: a hierarchical approach
• Multivariate Regression Analysis of Gravitational Waves from Rotating Core Collapse

Cited by in corpus (22)

• Deep Neural Networks to Enable Real-time Multimessenger Astrophysics
• Enhancing Gravitational-Wave Science with Machine Learning
• Accuracy of Estimating Highly Eccentric Binary Black Hole Parameters with Gravitational-Wave Detections
• Applying deep neural networks to the detection and space parameter estimation of compact binary coalescence with a network of gravitational wave detectors
• Application of dictionary learning to denoise LIGO's blip noise transients
• Space-based gravitational wave signal detection and extraction with deep neural network
• Binary Neutron Stars Gravitational Wave Detection Based on Wavelet Packet Analysis And Convolutional Neural Networks
• Total-variation methods for gravitational-wave denoising: performance tests on Advanced LIGO data
• Classification of gravitational-wave glitches via dictionary learning
• Applications of machine learning in gravitational wave research with current interferometric detectors
• Exploring gravitational-wave detection and parameter inference using Deep Learning methods
• Classification of the core-collapse supernova explosion mechanism with learned dictionaries
• Extraction of black hole coalescence waveforms from noisy data
• Using supervised learning algorithms as a follow-up method in the search of gravitational waves from core-collapse supernovae
• Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources
• Gravitational Wave Signal Extraction Against Non-Stationary Instrumental Noises with Deep Neural Network
• Noise reduction on single-shot images using an autoencoder
• Deep-Learning Classification and Parameter Inference of Rotational Core-Collapse Supernovae
• Comparison of neural network architectures for feature extraction from binary black hole merger waveforms
• Implementation of the rROF denoising method in the cWB pipeline for gravitational-wave data analysis
• Sliding Z Transform: Applications to convolutive blind source separation
• Extract non-Gaussian Features in Gravitational Wave Observation Data Using Self-Supervised Learning