Sci-Advent – Artificial Intelligence, High Performance Computing and Gravitational Waves
In a recent paper published in the ArXiV, researchers have highlighted the advantages that artificial intelligence techniques bring to the research of fields such as astrophysics. They are making their models available and that is always a great thing to see. They mention the use of these techniques to detect binary neutron stars, and to forecast the merger of multi-messenger sources, such as binary neutron stars and neutron star-black hole systems. Here are some highlights from the paper:
Finding new ways to use artificial intelligence (AI) to accelerate the analysis of gravitational wave data, and ensuring the developed models are easily reusable promises to unlock new opportunities in multi-messenger astrophysics (MMA), and to enable wider use, rigorous validation, and sharing of developed models by the community. In this work, we demonstrate how connecting recently deployed DOE and NSF-sponsored cyberinfrastructure allows for new ways to publish models, and to subsequently deploy these models into applications using computing platforms ranging from laptops to high performance computing clusters. We develop a workflow that connects the Data and Learning Hub for Science (DLHub), a repository for publishing machine learning models, with the Hardware Accelerated Learning (HAL) deep learning computing cluster, using funcX as a universal distributed computing service. We then use this workflow to search for binary black hole gravitational wave signals in open source advanced LIGO data. We find that using this workflow, an ensemble of four openly available deep learning models can be run on HAL and process the entire month of August 2017 of advanced LIGO data in just seven minutes, identifying all four binary black hole mergers previously identified in this dataset, and reporting no misclassifications. This approach, which combines advances in AI, distributed computing, and scientific data infrastructure opens new pathways to conduct reproducible, accelerated, data-driven gravitational wave detection.
Research and development of AI models for gravitational wave astrophysics is evolving at a rapid pace. In less than four years, this area of research has evolved from disruptive prototypes into sophisticated AI algorithms that describe the same 4-D signal manifold as traditional gravitational wave detection pipelines for binary black hole mergers, namely, quasi-circular, spinning, non- precessing, binary systems; have the same sensitivity as template matching algorithms; and are orders of magnitude faster, at a fraction of the computational cost.
AI models have been proven to effectively identify real gravitational wave signals in advanced LIGO data, including binary black hole and neutron stars mergers. The current pace of progress makes it clear that the broader community will continue to advance the development of AI tools to realize the science goals of Multi-Messenger Astrophysics.
Furthermore, mirroring the successful approach of corporations leading AI innovation in industry and technology, we are releasing our AI models to enable the broader community to use and perfect them. This approach is also helpful to address healthy and constructive skepticism from members of the community who do not feel at ease using AI algorithms.