An 18-year-old high school student harnessed artificial intelligence to uncover 1.5 million previously unknown cosmic objects.
Matteo Paz, from Pasadena, California, created a sophisticated machine learning algorithm that sifted through vast archives of data from NASA's NEOWISE telescope (the Near-Earth Object Wide-field Infrared Survey Explorer). Launched in 2009, NEOWISE spent over a decade surveying the sky in infrared wavelengths, originally hunting for near-Earth asteroids and comets while capturing billions of detections—roughly 200 billion in total—of celestial sources.
Hidden within this enormous dataset were subtle changes in infrared brightness that hint at dynamic phenomena: variable stars, supernovae explosions, feeding supermassive black holes, and close binary star systems, among others.
Rather than relying on manual inspection, Paz trained an AI model (including techniques like waveform analysis and his VARnet algorithm) to automatically detect and classify these faint variability signals across the entire collection. The result: a groundbreaking catalog named VarWISE, which identified about 1.9 million infrared variable objects overall, with 1.5 million representing entirely new discoveries never before cataloged by astronomers.
This VarWISE catalog is already aiding researchers in exploring unusual stellar behavior and other transient events across the universe.
Paz's achievement—conducted during research at Caltech under mentorship and culminating in a peer-reviewed paper—earned him first place and a $250,000 prize in the 2025 Regeneron Science Talent Search. It powerfully illustrates the transformation in modern astronomy: as telescopes generate data far beyond human processing capacity, pairing cutting-edge instruments with intelligent algorithms is unlocking hidden treasures right in existing archives.
The next big discoveries aren't always out in the distant cosmos—they're often buried in the data we've already collected.