Researchers have recently unveiled a novel approach to detecting gravitational wave sources, which they believe has the potential to significantly enhance the precision of detections and accelerate the identification of these enigmatic events.

The research, published in the journal Nature, proposes an algorithm for analyzing the gravitational wave emissions from neutron star mergers. By identifying these events, astronomers worldwide can be alerted, enabling them to gather as much information as possible about the transient and mysterious sources of gravitational waves.

To put this into perspective, gravitational waves are disturbances in space-time that were first predicted by Einstein over a century ago and were only directly observed for the first time in 2015 by the LIGO-Virgo-KAGRA Collaboration. These waves are produced by the interactions of extremely dense objects in the universe, including black holes and neutron stars.

The algorithm developed by the team focuses on neutron stars that are in a death spiral, gradually approaching each other until they merge, resulting in a “neutron star merger.” The detection of gravitational waves emitted by neutron stars and black holes aids astronomers! in understanding the structure of neutron stars, the origin of certain heavy elements, testing the theory of general relativity, measuring the universe’s expansion rate, and potentially providing insights into the nature of dark matter.

Artificial intelligence can accelerate the analysis of gravitational wave events and, as demonstrated by the team’s results, improve the accuracy of predicting the source merger’s location.

According to the team, “We present a machine-learning framework that enables complete binary neutron star inference in just 1 s [one second] without relying on approximations.” They further note, “We demonstrate that our method can be applied to long signals, up to an hour in length, serving as a foundation for data analysis in next-generation ground- and space-based detectors.”

The team’s algorithm exhibits a 30% improvement in accuracy compared to previous iterations, facilitating astronomers in determining which merger events require further observation, often under time-sensitive conditions.

As noted by Michael Williams, a cosmologist at the University of Portsmouth in the United Kingdom, in a News & Views article, “Machine learning has garnered significant attention in gravitational-wave research as a means to enhance or even replace existing analysis techniques.” However, Williams, who is not affiliated with the new research, cautions, “Several challenges persist, and the performance of machine-learning algorithms is generally highly dependent on their training. For this algorithm, a problem arises from the fact that the properties of real noise in gravitational-wave detectors vary over time, differing from the properties assumed during training, which can introduce systematic errors and bias results.”

Williams concludes that the “true test” of the algorithm will be its ability to provide information about the next binary neutron-star merger when it occurs.

The effectiveness of this machine-learning-based approach will be determined over time, but with cutting-edge observatories scheduled to come online in the near future, such as the Vera Rubin Observatory and its LSST Camera, detecting transient cosmic events as soon as possible will be crucial.