Detection of Einstein Telescope gravitational wave signals from binary black holes using deep learning

Abstract

The expected volume of data from the third-generation gravitational waves (GWs) Einstein Telescope (ET) detector would make traditional GWs search methods such as match filtering impractical. This is due to the large template bank required and the difficulties in waveforms modelling. In contrast, machine learning (ML) algorithms have shown a promising alternative for GWs data analysis, where ML can be used in developing semi-automatic and automatic tools for the detection, denoising and parameter estimation of GWs sources. Compared to second generation detectors, ET will have a wider accessible frequency band but also a lower noise. The ET will have a detection rate for Binary Black Holes (BBHs) and Binary Neutron Stars (BNSs) of order 1e5 - 1e6 per year and 7e4 per year respectively. In this work, we explore the possibility and efficiency of using convolutional neural networks (CNNs) for the detection of BBHs mergers in synthetic GWs signals buried in gaussian noise. The data was generated according to the ETs parameters using open-source tools. Without performing data whitening or applying bandpass filtering, we trained four CNN networks with the state-of-the-art performance in computer vision, namely VGG, ResNet and DenseNet. ResNet has significantly better performance, detecting BBHs sources with SNR of 8 or higher with 98.5% accuracy, and with 92.5%, 85%, 60% and 62% accuracy for sources with SNR range of 7-8, 6-7, 5-6 and 4-5 respectively. ResNet, in qualitative evaluation, was able to detect a BBHs merger at 60 Gpc with 4.3 SNR. It was also shown that, using CNN for BBHs merger on long time series data is computationally efficient, and can be used for near-real-time detection.

arXiv comment8 pages, 7 figures, accepted for publication in MNRAS

References in corpus (30)

• Deep Learning in Neural Networks: An Overview
• Observation of Gravitational Waves from a Binary Black Hole Merger
• Planck 2015 results. XIII. Cosmological parameters
• Deep Residual Learning for Image Recognition
• Deep Learning using Rectified Linear Units (ReLU)
• Densely Connected Convolutional Networks
• LIGO: The Laser Interferometer Gravitational-Wave Observatory
• Science Case for the Einstein Telescope
• Sensitivity Studies for Third-Generation Gravitational Wave Observatories
• Frequency-domain gravitational waves from non-precessing black-hole binaries. I. New numerical waveforms and anatomy of the signal
• ANNz: estimating photometric redshifts using artificial neural networks
• Method for detection and reconstruction of gravitational wave transients with networks of advanced detectors
• Star-galaxy Classification Using Deep Convolutional Neural Networks
• A Horizon Study for Cosmic Explorer: Science, Observatories, and Community
• Photometric redshift estimation via deep learning
• An Architecture Combining Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for Image Classification
• Forecasting the detection capabilities of third-generation gravitational-wave detectors using $\text{GWFAST}$
• Application of machine learning algorithms to the study of noise artifacts in gravitational-wave data
• Efficient Gravitational-wave Glitch Identification from Environmental Data Through Machine Learning
• Machine Learning Gravitational Waves from Binary Black Hole Mergers
• The FIRST Classifier: Compact and Extended Radio Galaxy Classification using Deep Convolutional Neural Networks
• Detecting Gravitational-waves from Extreme Mass Ratio Inspirals using Convolutional Neural Networks
• Constraints on the binary evolution from chirp mass measurements
• Convolutional neural networks for the detection of the early inspiral of a gravitational-wave signal
• Photometric redshift estimation using Gaussian processes
• Quantitative Morphology of Moderate Redshift Galaxies : How Many Peculiars are There ?
• Exploring compact binary populations with the Einstein Telescope
• Exploring gravitational-wave detection and parameter inference using Deep Learning methods
• Convolutional neural network for gravitational-wave early alert: Going down in frequency
• Morphology Classification and Photometric Redshift Measurement of Galaxies

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• New Gravitational Wave Discoveries Enabled by Machine Learning
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• Applications of machine learning in gravitational wave research with current interferometric detectors
• Novel Deep Learning Approach to Detecting Binary Black Hole Mergers