Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection

Abstract

The development of reusable artificial intelligence (AI) models for wider use and rigorous validation by the community promises to unlock new opportunities in multi-messenger astrophysics. Here we develop a workflow that connects the Data and Learning Hub for Science, a repository for publishing AI models, with the Hardware Accelerated Learning (HAL) cluster, using funcX as a universal distributed computing service. Using this workflow, an ensemble of four openly available AI models can be run on HAL to process an entire month's worth (August 2017) of advanced Laser Interferometer Gravitational-Wave Observatory data in just seven minutes, identifying all four all four binary black hole mergers previously identified in this dataset and reporting no misclassifications. This approach combines advances in AI, distributed computing, and scientific data infrastructure to open new pathways to conduct reproducible, accelerated, data-driven discovery.

17 pages, 5 figures; v2: 12 pages, 6 figures. Accepted to Nature Astronomy. See also the Behind the Paper blog in Nature Astronomy "https://astronomycommunity.nature.com/posts/from-disruption-to-sustained-innovation-artificial-intelligence-for-gravitational-wave-astrophysics"

References in corpus (12)

• GWTC-2: Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run
• Kilonovae
• Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science
• 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
• The LIGO Open Science Center
• funcX: A Federated Function Serving Fabric for Science
• Enabling real-time multi-messenger astrophysics discoveries with deep learning
• Detection of gravitational-wave signals from binary neutron star mergers using machine learning
• Gravitational-wave surrogate models powered by artificial neural networks: The ANN-Sur for waveform generation
• Deep Learning Ensemble for Real-time Gravitational Wave Detection of Spinning Binary Black Hole Mergers
• Deep learning for gravitational wave forecasting of neutron star mergers
• BOSS-LDG: A Novel Computational Framework that Brings Together Blue Waters, Open Science Grid, Shifter and the LIGO Data Grid to Accelerate Gravitational Wave Discovery

Cited by in corpus (31)

• FAIR for AI: An interdisciplinary and international community building perspective
• MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge
• FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy
• Applications of physics informed neural operators
• Deep Residual Networks for Gravitational Wave Detection
• Early warning of coalescing neutron-star and neutron-star-black-hole binaries from nonstationary noise background using neural networks
• From One to Many: A Deep Learning Coincident Gravitational-Wave Search
• Deep Learning Detection and Classification of Gravitational Waves from Neutron Star-Black Hole Mergers
• Inference-optimized AI and high performance computing for gravitational wave detection at scale
• Ensemble of Deep Convolutional Neural Networks for real-time gravitational wave signal recognition
• Deep Learning with Quantized Neural Networks for Gravitational Wave Forecasting of Eccentric Compact Binary Coalescence
• New Gravitational Wave Discoveries Enabled by Machine Learning
• Rapid search for massive black hole binary coalescences using deep learning
• Utilizing Gaussian mixture models in all-sky searches for short-duration gravitational wave bursts
• Applications of machine learning in gravitational wave research with current interferometric detectors
• Interpretable AI forecasting for numerical relativity waveforms of quasi-circular, spinning, non-precessing binary black hole mergers
• Searches for Mass-Asymmetric Compact Binary Coalescence Events using Neural Networks in the LIGO/Virgo Third Observation Period
• Robust inference of gravitational wave source parameters in the presence of noise transients using normalizing flows
• FAIR AI Models in High Energy Physics
• End-to-end AI framework for interpretable prediction of molecular and crystal properties
• Searching for gravitational waves from stellar-mass binary black holes early inspiral
• Novel Deep Learning Approach to Detecting Binary Black Hole Mergers
• 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
• Physics-inspired spatiotemporal-graph AI ensemble for the detection of higher order wave mode signals of spinning binary black hole mergers
• Compact Binary Coalescence Gravitational Wave Signals Counting and Separation
• Time-inversion of spatiotemporal beam dynamics using uncertainty-aware latent evolution reversal
• GWAI: Artificial Intelligence Platform for Enhanced Gravitational Wave Data Analysis
• A FAIR and AI-ready Higgs boson decay dataset
• Sequence modeling of higher-order wave modes of binary black hole mergers
• Conditional variational autoencoders for cosmological model discrimination and anomaly detection in cosmic microwave background power spectra
• Extract non-Gaussian Features in Gravitational Wave Observation Data Using Self-Supervised Learning