AI speeds up detection of neutron star mergers

A new artificial intelligence/machine learning method rapidly and accurately characterizes binary neutron star mergers based on the gravitational wave signature they produce. Though the method has not yet been tested on new mergers happening “live”, it could enable astronomers to make quicker estimates of properties such as the location of mergers and the masses of the neutron stars. This information, in turn, could make it possible for telescopes to target and observe the electromagnetic signals that accompany such mergers.

Gravitational Waves and Binary Neutron Star Mergers

When massive objects such as black holes and neutron stars collide and merge, they emit ripples in spacetime known as gravitational waves (GWs). In 2015, scientists on Earth began observing these ripples using kilometre-scale interferometers that measure the minuscule expansion and contraction of space-time that occurs when a gravitational wave passes through our planet. These interferometers are located in the US, Italy, and Japan and are known collectively as the LVK observatories after their initials: the Laser Interferometer GW Observatory (LIGO), the Virgo GW Interferometer (Virgo), and the Kamioka GW Detector (KAGRA).

When two neutron stars in a binary pair merge, they emit electromagnetic waves as well as GWs. While both types of wave travel at the speed of light, certain poorly-understood processes that occur within and around the merging pair cause the electromagnetic signal to be slightly delayed. This means that the LVK observatories can detect the GW signal coming from a binary neutron star (BNS) merger seconds, or even minutes, before its electromagnetic counterpart arrives. Being able to identify GWs quickly and accurately therefore increases the chances of detecting other signals from the same event.

Machine Learning for Rapid Characterization

Physicists led by Maximilian Dax of the Max Planck Institute for Intelligent Systems in Tübingen, Germany have now developed a machine learning (ML) framework that accurately characterizes and localizes BNS mergers within a second of a GW being detected, without resorting to such approximations. To do this, they trained a deep neural network model with millions of GW simulations. Once trained, the neural network can take fresh GW data as input and predict corresponding properties of the merging BNSs – for example, their masses, locations, and spins – based on its training dataset.

Crucially, this neural network output includes a sky map. This map provides a fast and accurate estimate for where the BNS is located. The new work built on the group’s previous studies, which used ML systems to analyze GWs from binary black hole (BBH) mergers. “Fast inference is more important for BNS mergers, however,” Dax says, “to allow for quick searches for the aforementioned electromagnetic counterparts, which are not emitted by BBH mergers.”

Future Implications and Applications

The researchers, who report their work in Nature, hope their method will help astronomers to observe electromagnetic counterparts for BNS mergers more often and detect them earlier – that is, closer to when the merger occurs. Being able to do this could reveal important information on the underlying processes that occur during these events. “It could also serve as a blueprint for dealing with the increased GW signal duration that we will encounter in the next generation of GW detectors,” Dax says. “This could help address a critical challenge in future GW data analysis.”

So far, the team has focused on data from current GW detectors (LIGO and Virgo) and has only briefly explored next-generation ones. They now plan to apply their method to these new GW detectors in more depth.

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