Gravitational wave signal recognition of O1 data by deep learning

Abstract

Deep learning method develops very fast as a tool for data analysis these years. Such a technique is quite promising to treat gravitational wave detection data. There are many works already in the literature which used deep learning technique to process simulated gravitational wave data. In this paper we apply deep learning to LIGO O1 data. In order to improve the weak signal recognition we adjust the convolutional neural network (CNN) a little bit. Our adjusted convolutional neural network admits comparable accuracy and efficiency of signal recognition as other deep learning works published in the literature. Based on our adjusted CNN, we can clearly recognize the eleven confirmed gravitational wave events included in O1 and O2. And more we find about 2000 gravitational wave triggers in O1 data.

arXiv comment7 pages; 5 figures

References in corpus (45)

• Observation of Gravitational Waves from a Binary Black Hole Merger
• GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral
• 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
• GW170814: A Three-Detector Observation of Gravitational Waves from a Binary Black Hole Coalescence
• GW170608: Observation of a 19-solar-mass Binary Black Hole Coalescence
• Binary Black Hole Mergers in the first Advanced LIGO Observing Run
• Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data
• Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science
• Deep Neural Networks to Enable Real-time Multimessenger Astrophysics
• The LIGO Open Science Center
• Matching matched filtering with deep networks in gravitational-wave astronomy
• 2-OGC: Open Gravitational-wave Catalog of binary mergers from analysis of public Advanced LIGO and Virgo data
• 1-OGC: The first open gravitational-wave catalog of binary mergers from analysis of public Advanced LIGO data
• A waveform model for eccentric binary black hole based on effective-one-body-numerical-relativity (EOBNR) formalism
• Glitch Classification and Clustering for LIGO with Deep Transfer Learning
• Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO
• Cosmological constraints with deep learning from KiDS-450 weak lensing maps
• What is the Machine Learning?
• Convolutional neural networks: a magic bullet for gravitational-wave detection?
• Learning Bayesian posteriors with neural networks for gravitational-wave inference
• Non-Gaussian information from weak lensing data via deep learning
• Real-Time Detection of Gravitational Waves from Binary Neutron Stars using Artificial Neural Networks
• Gravitational Wave Denoising of Binary Black Hole Mergers with Deep Learning
• Deep Recurrent Neural Networks for Supernovae Classification
• Weak lensing cosmology with convolutional neural networks on noisy data
• Image-based deep learning for classification of noise transients in gravitational wave detectors
• Reduced-order modeling with artificial neurons for gravitational-wave inference
• Deep-Learning continuous gravitational waves
• Classification methods for noise transients in advanced gravitational-wave detectors
• Transient Classification in LIGO data using Difference Boosting Neural Network
• A New Method to Observe Gravitational Waves emitted by Core Collapse Supernovae
• How effective is machine learning to detect long transient gravitational waves from neutron stars in a real search?
• Classification methods for noise transients in advanced gravitational-wave detectors II: performance tests on Advanced LIGO data
• Applying deep neural networks to the detection and space parameter estimation of compact binary coalescence with a network of gravitational wave detectors
• (Machine) Learning to Do More with Less
• Methods for Reducing False Alarms in Searches for Compact Binary Coalescences in LIGO Data
• Using Deep Learning to Localize Gravitational Wave Sources
• Denoising Weak Lensing Mass Maps with Deep Learning
• Fusing numerical relativity and deep learning to detect higher-order multipole waveforms from eccentric binary black hole mergers
• Analyzing γ-rays of the Galactic Center with Deep Learning
• Neural Network Aided Glitch-Burst Discrimination and Glitch Classification
• Regression methods in waveform modeling: a comparative study
• Identifying extra high frequency gravitational waves generated from oscillons with cuspy potentials using deep neural networks

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• Machine Learning for Nanohertz Gravitational Wave Detection and Parameter Estimation with Pulsar Timing Array
• Constraints on typical relic gravitational waves based on data of LIGO