AI Breakthrough Enables Near-Instant Analysis of Neutron Star Mergers

A new machine learning algorithm drastically improves the speed and accuracy of gravitational wave detection, aiding real-time astronomical observations.

Scientists have developed a neural network, DINGO-BNS, that analyzes gravitational waves from neutron star mergers in about one second, compared to an hour with traditional methods.
The algorithm improves localization accuracy by 30%, allowing telescopes to quickly target and observe accompanying electromagnetic signals like kilonovas.
Real-time analysis could enable astronomers to witness neutron star collisions as they happen, offering new insights into the production of heavy elements and the merger process.
The machine learning approach combines advanced AI techniques with physical domain knowledge, addressing limitations of current rapid analysis methods used by observatories like LIGO-Virgo-KAGRA.
Researchers acknowledge challenges such as adapting the algorithm to real-world noise in gravitational wave detectors but highlight its potential for next-generation observatories.