Rohan Arni, 17, used deep learning to study mysterious space signals with 98% accuracy; now he is a US Regeneron STS fin
Seventeen-year-old Rohan Arni is making waves in the field of astronomy by utilizing deep learning to investigate fast radio bursts (FRBs), enigmatic signals that emit powerful flashes of radio waves across the universe. His innovative machine-learning model has achieved an impressive 98% accuracy in classifying these bursts, distinguishing between repeating and non-repeating signals. This remarkable achievement has earned him a spot as a finalist in the prestigious 2026 Regeneron Science Talent Search, where he stands out among 40 finalists selected from over 2,600 participants nationwide. Rohan's project, titled “Deep Learning for Classification of Fast Radio Bursts,” leverages data from the Canadian Hydrogen Intensity Mapping Experiment (CHIME), a cutting-edge radio telescope. By automating the analysis of vast datasets, his model not only classifies FRBs but also uncovers potential differences between repeating and non-repeating bursts. His findings suggest that these two categories may originate from distinct cosmic phenomena, a significant insight into the ongoing investigation of FRB origins. Beyond his research, Rohan is deeply engaged in the scientific community, having collaborated with Harvard researchers on physics-informed neural networks. His passion for STEM extends to mentoring younger students and volunteering, showcasing his commitment to sharing knowledge and fostering curiosity. As he continues to explore the mysteries of the universe, Rohan exemplifies how young minds can contribute meaningfully to scientific advancement.
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