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Related Experiment Video

Updated: Oct 1, 2025

The Generation of Higher-order Laguerre-Gauss Optical Beams for High-precision Interferometry
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Inference-Optimized AI and High Performance Computing for Gravitational Wave Detection at Scale.

Pranshu Chaturvedi1,2,3, Asad Khan1,3,4, Minyang Tian3,4

  • 1Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, United States.

Frontiers in Artificial Intelligence
|March 7, 2022
PubMed
Summary
This summary is machine-generated.

We developed an AI ensemble for gravitational wave detection, achieving a 3x speedup over traditional models. This advanced system efficiently processes vast amounts of data, maintaining high sensitivity for detecting cosmic events.

Keywords:
AIGPU-accelerated computingHPCblack holesgravitational waves

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Area of Science:

  • Astrophysics
  • Computer Science
  • Artificial Intelligence

Background:

  • Gravitational wave detection is crucial for understanding cosmic events.
  • Traditional methods face computational challenges with increasing data volumes.
  • Artificial intelligence offers potential for faster and more efficient data analysis.

Purpose of the Study:

  • To develop and optimize an AI ensemble for accelerated gravitational wave detection.
  • To evaluate the performance and sensitivity of the AI ensemble on large datasets.
  • To demonstrate the scalability of AI-driven gravitational wave detection.

Main Methods:

  • Training an ensemble of AI models on the Summit supercomputer.
  • Optimizing models for accelerated inference using NVIDIA TensorRT.
  • Deploying the optimized AI ensemble on the ThetaGPU supercomputer for distributed inference.
  • Processing advanced LIGO data, including Hanford and Livingston streams.

Main Results:

  • The AI ensemble processed one month of advanced LIGO data in 50 seconds, achieving a 3x inference speedup.
  • The system demonstrated sensitivity comparable to traditional models, identifying all known binary black hole mergers without misclassifications.
  • Analysis of 5 years of data showed an average of one misclassification per month, with a presented receiver operating characteristic curve.

Conclusions:

  • The developed AI ensemble provides a significant speedup for gravitational wave detection.
  • This approach enables accelerated, AI-driven gravitational wave detection at scale.
  • The optimized AI models offer a powerful tool for analyzing large astrophysical datasets.