# Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection

arXiv: https://arxiv.org/abs/2012.08545 · HTML: https://mlsanity.com/paper/2012.08545/

## Key facts

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

## Built on by (most cited first, 20 of 20)

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- [Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders](https://mlsanity.com/paper/2107.12698/) (2021)
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- [FAIR AI Models in High Energy Physics](https://mlsanity.com/paper/2212.05081/) (2022)
- [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](https://mlsanity.com/paper/2112.07669/) (2021)
- [Decoding complexity: how machine learning is redefining scientific discovery](https://mlsanity.com/paper/2405.04161/) (2024)
- [Interpreting a Machine Learning Model for Detecting Gravitational Waves](https://mlsanity.com/paper/2202.07399/) (2022)
- [A Neural Network-Based Search for Unmodeled Transients in LIGO-Virgo-KAGRA's Third Observing Run](https://mlsanity.com/paper/2412.19883/) (2024)
- [Testing General Relativity Through Gravitational Wave Classification: A Convolutional Neural Network Framework](https://mlsanity.com/paper/2605.02453/) (2026)
- [AI ensemble for signal detection of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers](https://mlsanity.com/paper/2310.00052/) (2023)
- [Transfer Learning Adapts to Changing PSD in Gravitational Wave Data](https://mlsanity.com/paper/2410.11911/) (2024)
- [AI-enabled gravitational-waves searches for binary neutron stars at optimal sensitivity](https://mlsanity.com/paper/2607.01372/) (2026)

## 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.