Binary neutron star mergers emit gravitational waves followed by light. To fully exploit these observations and avoid missing key signals, speed is crucial. In a study to be published in Nature on March 5, 2025, an interdisciplinary team of researchers presents a novel machine learning method that can analyze gravitational waves emitted by neutron star collisions almost instantaneously – even before the merger is fully observed. A neural network processes the data and enables a fast search for visible light and other electromagnetic signals emitted during the collisions. This new method could be instrumental in preparing the field for the next generation of observatories.

Using the Gemini South telescope, one half of the International Gemini Observatory, partly funded by the U.S. National Science Foundation and operated by NSF NOIRLab, and the Magellan Baade Telescope, astronomers have for the first time observed a recurring nova outside of the Milky Way in near-infrared light. The data revealed highly unusual chemical emissions as well as one of the hottest temperatures ever reported for a nova, both indicative of an extremely violent eruption.