AI Breakthrough Speeds Up Gravitational Wave Analysis from Neutron Star Mergers

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Binary neutron star mergers occur millions of light-years away, producing gravitational waves that carry crucial information about the extreme physics of these cosmic events. However, analyzing these signals is a major challenge for traditional data-processing methods. Current detectors record minutes of data for each event, while future observatories may capture signals lasting hours or even days. Handling such massive datasets is computationally expensive and time-consuming.

Now, an international team of scientists has developed DINGO-BNS (Deep INference for Gravitational-wave Observations from Binary Neutron Stars), a machine learning algorithm that dramatically accelerates the interpretation of gravitational waves from these mergers. Using a neural network trained for real-time analysis, DINGO-BNS can fully characterize merging neutron star systems in just one second, compared to nearly an hour for the fastest traditional techniques. Their study will be published in Nature on March 5, 2025, under the title "Real-time inference for binary neutron star mergers using machine learning."
Advancing Rapid Detection for Multi-Messenger Astronomy

Neutron star mergers not only generate gravitational waves but also produce electromagnetic signals, including visible light from kilonova explosions. Detecting these signals requires astronomers to act quickly, pointing telescopes in the right direction as soon as possible.

"Rapid and accurate gravitational-wave analysis is crucial for localizing the source and ensuring that telescopes can observe all the accompanying signals," explains Maximilian Dax, first author of the study and a Ph.D. student at the Max Planck Institute for Intelligent Systems (MPI-IS), ETH Zurich, and the ELLIS Institute Tübingen.

The real-time capabilities of DINGO-BNS could set a new standard for gravitational-wave data analysis, helping astronomers respond faster to detections made by the LIGO-Virgo-KAGRA (LVK) collaboration.

"Current rapid analysis methods used by LVK make approximations that trade accuracy for speed. Our study addresses these limitations," says Jonathan Gair, a group leader at the Max Planck Institute for Gravitational Physics in Potsdam Science Park.

Unlike existing methods, DINGO-BNS fully characterizes key properties of the merger—including masses, spins, and location—without needing approximations. This results in a 30% improvement in sky localization, allowing astronomers to quickly and precisely pinpoint the source of the merger.
Machine Learning Meets Gravitational Physics

Analyzing gravitational waves from binary neutron stars is particularly challenging, requiring innovative computational techniques. The development of DINGO-BNS introduced several new technical solutions, including event-adaptive data compression, which optimizes how gravitational wave data is processed.

"Our study highlights how modern machine learning methods can be effectively combined with physical domain knowledge," says Bernhard Schölkopf, Director of the Empirical Inference Department at MPI-IS and the ELLIS Institute Tübingen.
Unlocking Early Multi-Messenger Observations

DINGO-BNS could one day make it possible to observe electromagnetic signals before and during the neutron star collision itself. Capturing these signals in real time could provide unprecedented insights into the merger process and the subsequent kilonova explosion—phenomena that remain shrouded in mystery.

"Early multi-messenger observations could reveal new details about these violent cosmic events," says Alessandra Buonanno, Director of the Astrophysical and Cosmological Relativity Department at the Max Planck Institute for Gravitational Physics.

By drastically improving the speed and precision of gravitational-wave data analysis, DINGO-BNS marks a significant leap forward in astrophysics, paving the way for new discoveries in the study of neutron star mergers and multi-messenger astronomy.