Rohan Arni, 17, a student at High Technology High School in Lincroft, New Jersey, developed a machine-learning model that classified repeating and non-repeating fast radio bursts (FRBs) with 98% accuracy. His research earned him a place among the 40 finalists of the 2026 Regeneron Science Talent Search, selected from more than 2,600 entrants representing 826 high schools across 46 states, Washington, D.C., Puerto Rico, the Northern Mariana Islands and 16 countries. For his project, titled “Deep Learning for Classification of Fast Radio Bursts,” he used data collected by the Canadian Hydrogen Intensity Mapping Experiment (CHIME). His analysis suggested that repeating FRBs tend to be closer to Earth and have smaller frequency ranges than non-repeating FRBs, indicating the two categories may come from different places in the universe.