Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources

Abstract

We live in momentous times. The science community is empowered with an arsenal of cosmic messengers to study the Universe in unprecedented detail. Gravitational waves, electromagnetic waves, neutrinos and cosmic rays cover a wide range of wavelengths and time scales. Combining and processing these datasets that vary in volume, speed and dimensionality requires new modes of instrument coordination, funding and international collaboration with a specialized human and technological infrastructure. In tandem with the advent of large-scale scientific facilities, the last decade has experienced an unprecedented transformation in computing and signal processing algorithms. The combination of graphics processing units, deep learning, and the availability of open source, high-quality datasets, have powered the rise of artificial intelligence. This digital revolution now powers a multi-billion dollar industry, with far-reaching implications in technology and society. In this chapter we describe pioneering efforts to adapt artificial intelligence algorithms to address computational grand challenges in Multi-Messenger Astrophysics. We review the rapid evolution of these disruptive algorithms, from the first class of algorithms introduced in early 2017, to the sophisticated algorithms that now incorporate domain expertise in their architectural design and optimization schemes. We discuss the importance of scientific visualization and extreme-scale computing in reducing time-to-insight and obtaining new knowledge from the interplay between models and data.

arXiv comment30 pages, 11 figures. Invited chapter for "Handbook of Gravitational Wave Astronomy"; v2: updated to reflect published version

References in corpus (89)

• WaveNet: A Generative Model for Raw Audio
• Evolution of Binary Black Hole Spacetimes
• Accurate Evolutions of Orbiting Black-Hole Binaries Without Excision
• Gravitational wave extraction from an inspiraling configuration of merging black holes
• A gravitational-wave standard siren measurement of the Hubble constant
• Frequency-domain gravitational waves from non-precessing black-hole binaries. II. A phenomenological model for the advanced detector era
• Robust parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library
• A simple model of complete precessing black-hole-binary gravitational waveforms
• 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
• 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
• Deep Learning and its Application to LHC Physics
• 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
• Eccentric Black Hole Mergers Forming in Globular Clusters
• On the gravitational field of a mass point according to Einstein's theory
• First measurement of the Hubble constant from a dark standard siren using the Dark Energy Survey galaxies and the LIGO/Virgo binary-black-hole merger GW170814
• PyCBC Inference: A Python-based parameter estimation toolkit for compact binary coalescence signals
• Deep Neural Networks to Enable Real-time Multimessenger Astrophysics
• Surrogate model of hybridized numerical relativity binary black hole waveforms
• Spectroscopy of Kerr black holes with Earth- and space-based interferometers
• Matching matched filtering with deep networks in gravitational-wave astronomy
• A Numerical Relativity Waveform Surrogate Model for Generically Precessing Binary Black Hole Mergers
• Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy
• Georgia Tech Catalog of Gravitational Waveforms
• Black Hole Based Tests of General Relativity
• A Data Ecosystem to Support Machine Learning in Materials Science
• Glitch Classification and Clustering for LIGO with Deep Transfer Learning
• Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO
• Gravitational-wave parameter estimation with autoregressive neural network flows
• A Surrogate Model of Gravitational Waveforms from Numerical Relativity Simulations of Precessing Binary Black Hole Mergers
• Eccentric, nonspinning, inspiral, Gaussian-process merger approximant for the detection and characterization of eccentric binary black hole mergers
• Testing gravitational-wave searches with numerical relativity waveforms: Results from the first Numerical INJection Analysis (NINJA) project
• Learning Bayesian posteriors with neural networks for gravitational-wave inference
• Machine-learning non-stationary noise out of gravitational wave detectors
• The RIT binary black hole simulations catalog
• Recent searches for continuous gravitational waves
• 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
• Use of the MultiNest algorithm for gravitational wave data analysis
• Model-independent inference on compact-binary observations
• Image-based deep learning for classification of noise transients in gravitational wave detectors
• Surrogate model for an aligned-spin effective one body waveform model of binary neutron star inspirals using Gaussian process regression
• Reduced-order modeling with artificial neurons for gravitational-wave inference
• A Surrogate Model for Gravitational Wave Signals from Comparable- to Large- Mass-Ratio Black Hole Binaries
• Deep-Learning continuous gravitational waves
• Gravitational wave signal recognition of O1 data by deep learning
• Noise Reduction in Gravitational-wave Data via Deep Learning
• Enabling real-time multi-messenger astrophysics discoveries with deep learning
• The Third RIT binary black hole simulations catalog
• How effective is machine learning to detect long transient gravitational waves from neutron stars in a real search?
• Detection of gravitational-wave signals from binary neutron star mergers using machine learning
• Finding the origin of noise transients in LIGO data with machine learning
• Applying deep neural networks to the detection and space parameter estimation of compact binary coalescence with a network of gravitational wave detectors
• Convergence of Artificial Intelligence and High Performance Computing on NSF-supported Cyberinfrastructure
• Template banks to search for low-mass binary black holes in advanced gravitational-wave detectors
• Rotating stellar core-collapse waveform decompositon: a Principal Component Analysis approach
• Gravitational-wave surrogate models powered by artificial neural networks: The ANN-Sur for waveform generation
• Gravitational waveforms from SpEC simulations : neutron star-neutron star and low-mass black hole-neutron star binaries
• Efficient Gravitational-wave Glitch Identification from Environmental Data Through Machine Learning
• Deep learning for clustering of continuous gravitational wave candidates
• Denoising of gravitational wave signals via dictionary learning algorithms
• Deep Learning Ensemble for Real-time Gravitational Wave Detection of Spinning Binary Black Hole Mergers
• Deep-Learning Continuous Gravitational Waves: Multiple detectors and realistic noise
• A Precessing Numerical Relativity Waveform Surrogate Model for Binary Black Holes: A Gaussian Process Regression Approach
• A Machine Learning Based Source Property Inference for Compact Binary Mergers
• Deep learning for gravitational wave forecasting of neutron star mergers
• Classifying the unknown: discovering novel gravitational-wave detector glitches using similarity learning
• Deep Learning at Scale for the Construction of Galaxy Catalogs in the Dark Energy Survey
• Physics of eccentric binary black hole mergers: A numerical relativity perspective
• Some Optimizations on Detecting Gravitational Wave Using Convolutional Neural Network
• Detection of Gravitational Waves Using Bayesian Neural Networks
• Application of dictionary learning to denoise LIGO's blip noise transients
• Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders
• The GstLAL Search Analysis Methods for Compact Binary Mergers in Advanced LIGO's Second and Advanced Virgo's First Observing Runs
• Denoising Gravitational Waves using Deep Learning with Recurrent Denoising Autoencoders
• Comparison of various methods to extract ringdown frequency from gravitational wave data
• Fusing numerical relativity and deep learning to detect higher-order multipole waveforms from eccentric binary black hole mergers
• Total-variation-based methods for gravitational wave denoising
• Deep Learning with Quantized Neural Networks for Gravitational Wave Forecasting of Eccentric Compact Binary Coalescence
• Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers
• Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms
• Rapid and accurate parameter inference for coalescing, precessing compact binaries
• Artificial neural network subgrid models of 2-D compressible magnetohydrodynamic turbulence
• Unmodelled Clustering Methods for Gravitational Wave Populations of Compact Binary Mergers
• Gravitational-wave signal-to-noise interpolation via neural networks
• Statistically-informed deep learning for gravitational wave parameter estimation

Cited by in corpus (8)

• FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy
• Applications of physics informed neural operators
• Training Strategies for Deep Learning Gravitational-Wave Searches
• Inference-optimized AI and high performance computing for gravitational wave detection at scale
• Interpretable AI forecasting for numerical relativity waveforms of quasi-circular, spinning, non-precessing binary black hole mergers
• Neural network time-series classifiers for gravitational-wave searches in single-detector periods
• 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
• A FAIR and AI-ready Higgs boson decay dataset