Enhancing Gravitational-Wave Science with Machine Learning

Abstract

Machine learning has emerged as a popular and powerful approach for solving problems in astrophysics. We review applications of machine learning techniques for the analysis of ground-based gravitational-wave detector data. Examples include techniques for improving the sensitivity of Advanced LIGO and Advanced Virgo gravitational-wave searches, methods for fast measurements of the astrophysical parameters of gravitational-wave sources, and algorithms for reduction and characterization of non-astrophysical detector noise. These applications demonstrate how machine learning techniques may be harnessed to enhance the science that is possible with current and future gravitational-wave detectors.

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Cited by in corpus (91)

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• Training Strategies for Deep Learning Gravitational-Wave Searches
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• Measurement of the Depth of Maximum of Air-Shower Profiles with energies between $10^{18.5}$ and $10^{20}$ eV using the Surface Detector of the Pierre Auger Observatory and Deep Learning
• Premerger detection of massive black hole binaries using deep learning
• Gravitational wave surrogates through automated machine learning
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• Visualizing convolutional neural network for classifying gravitational waves from core-collapse supernovae
• Localization of gravitational waves using machine learning
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• A Coincidence Null Test for Poisson-Distributed Events
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• Convolutional Neural Networks for signal detection in real LIGO data
• Binary Neutron Star Merger Search Pipeline Powered by Deep Learning
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• Reconstruction of binary black hole harmonics in LIGO using deep learning
• cDVGAN: One Flexible Model for Multi-class Gravitational Wave Signal and Glitch Generation
• Navigating Unknowns: Deep Learning Robustness for Gravitational Wave Signal Reconstruction
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• AI and extreme scale computing to learn and infer the physics of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers
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