Like its human counterparts, a young star is cute but prone to temper flares—only a star’s are lethal. A flare from a star can incinerate everything around it, including the atmospheres of any nearby planets starting to form.
Finding out how often such young stars erupt can help scientists understand where to look for habitable planets. But until now, locating such flares involved poring over thousands of measurements of star brightness variations, called “light curves,” by eye.
Scientists with the University of Chicago and the University of New South Wales, however, thought this would be a task well suited for machine learning. They taught a type of artificial intelligence called a neural network to detect the telltale light patterns of a stellar flare, then asked it to check the light curves of thousands of young stars; it found more than 23,000 flares.
Published Oct. 23 in the Astronomical Journal and the Journal of Open Source Software the results offer a new benchmark in the use of AI in astronomy, as well as a better understanding of the evolution of young stars and their planets.
“When we say young, we mean only a million to 800 million years old,” said Adina Feinstein, a UChicago graduate student and first author on the paper. “Any planets near a star are still forming at this point. This is a particularly fragile time, and a flare from a star can easily evaporate any water or atmosphere that’s been collected.”
NASA’s TESS telescope, aboard a satellite that has been orbiting Earth since 2018, is specifically designed to search for exoplanets. Flares from faraway stars show up on TESS’s images, but traditional algorithms have a hard time picking out the shape from the background noise of star activity.