Denoising Gravitational Waves using Deep Learning with Recurrent Denoising Autoencoders

Abstract

Gravitational wave astronomy is a rapidly growing field of modern astrophysics, with observations being made frequently by the LIGO detectors. Gravitational wave signals are often extremely weak and the data from the detectors, such as LIGO, is contaminated with non-Gaussian and non-stationary noise, often containing transient disturbances which can obscure real signals. Traditional denoising methods, such as principal component analysis and dictionary learning, are not optimal for dealing with this non-Gaussian noise, especially for low signal-to-noise ratio gravitational wave signals. Furthermore, these methods are computationally expensive on large datasets. To overcome these issues, we apply state-of-the-art signal processing techniques, based on recent groundbreaking advancements in deep learning, to denoise gravitational wave signals embedded either in Gaussian noise or in real LIGO noise. We introduce SMTDAE, a Staired Multi-Timestep Denoising Autoencoder, based on sequence-to-sequence bi-directional Long-Short-Term-Memory recurrent neural networks. We demonstrate the advantages of using our unsupervised deep learning approach and show that, after training only using simulated Gaussian noise, SMTDAE achieves superior recovery performance for gravitational wave signals embedded in real non-Gaussian LIGO noise.

arXiv comment5 pages, 2 figures

References in corpus (7)

• Observation of Gravitational Waves from a Binary Black Hole Merger
• GW151226: Observation of Gravitational Waves from a 22-Solar-Mass Binary Black Hole Coalescence
• The PyCBC search for gravitational waves from compact binary coalescence
• An improved effective-one-body model of spinning, nonprecessing binary black holes for the era of gravitational-wave astrophysics with advanced detectors
• Deep Neural Networks to Enable Real-time Multimessenger Astrophysics
• Implementing a search for aligned-spin neutron star -- black hole systems with advanced ground based gravitational wave detectors
• Complete waveform model for compact binaries on eccentric orbits

Cited by in corpus (15)

• The Science of the Einstein Telescope
• Enabling real-time multi-messenger astrophysics discoveries with deep learning
• Astronomia ex machina: a history, primer, and outlook on neural networks in astronomy
• MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge
• Detecting Gravitational-waves from Extreme Mass Ratio Inspirals using Convolutional Neural Networks
• On Neural Architectures for Astronomical Time-series Classification with Application to Variable Stars
• A review of unsupervised learning in astronomy
• Space-based gravitational wave signal detection and extraction with deep neural network
• Inference-optimized AI and high performance computing for gravitational wave detection at scale
• Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms
• Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources
• SLICK: Strong Lensing Identification of Candidates Kindred in gravitational wave data
• De-noising of galaxy optical spectra with autoencoders
• Mass and tidal parameter extraction from gravitational waves of binary neutron stars mergers using deep learning
• Data-driven detector signal characterization with constrained bottleneck autoencoders