mlsanityData to 7 Oct 2026

Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection

arXiv 2012.08545 ↗First posted 15 Dec 2020gr-qc astro-ph.IM cs.AIcited by 20

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Dec 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 7; 2023: 1; 2024: 5; 2025: 0; 2026: 2.

20 papers.

Sort:

  1. 2022Applications of physics informed neural operatorscited by 33
  2. 2021A FAIR and AI-ready Higgs boson decay datasetcited by 14
  3. 2022FAIR for AI: An interdisciplinary and international community building perspectivecited by 13
  4. 2021Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sourcescited by 10
  5. 2022FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopycited by 10
  6. 2021Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoderscited by 7
  7. 2024A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle acceleratorscited by 7
  8. 2024Time-inversion of spatiotemporal beam dynamics using uncertainty-aware latent evolution reversalcited by 6
  9. 2021Interpretable AI forecasting for numerical relativity waveforms of quasi-circular, spinning, non-precessing binary black hole mergerscited by 5
  10. 2022Inference-optimized AI and high performance computing for gravitational wave detection at scalecited by 5
  11. 2022MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challengecited by 4
  12. 2022FAIR AI Models in High Energy Physicscited by 4
8 more
  1. 2021AI 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 mergerscited by 3
  2. 2024Decoding complexity: how machine learning is redefining scientific discoverycited by 3
  3. 2022Interpreting a Machine Learning Model for Detecting Gravitational Wavescited by 2
  4. 2024A Neural Network-Based Search for Unmodeled Transients in LIGO-Virgo-KAGRA's Third Observing Runcited by 1
  5. 2026Testing General Relativity Through Gravitational Wave Classification: A Convolutional Neural Network Frameworkcited by 1
  6. 2023AI ensemble for signal detection of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergerscited by 0
  7. 2024Transfer Learning Adapts to Changing PSD in Gravitational Wave Datacited by 0
  8. 2026AI-enabled gravitational-waves searches for binary neutron stars at optimal sensitivitycited by 0