A talented high school researcher collaborating with Caltech scientists has crafted an advanced AI-powered algorithm that identified over 1.5 million previously unknown celestial bodies by mining NASA's dormant NEOWISE telescope data. This breakthrough earned a solo publication in The Astronomical Journal and promises to revolutionize studies on cosmic variability.
An Amateur Astronomer’s Journey to Major Discovery
The inspiring journey traces back to Matteo Paz, a high schooler driven by a fascination with space since attending public astronomy talks at Caltech. In 2022, he joined the Planet Finder Academy, gaining invaluable mentorship from Davy Kirkpatrick, a senior scientist at IPAC.
“I’m so lucky to have met Davy,” Paz said. “I remember the first day I talked to him, I said that I was considering working on a paper to come out of this, which is a much larger goal than six weeks. He didn’t discourage me. He said, ‘OK, so let’s talk about that.’”
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Kirkpatrick, who credits his own high school guide for inspiring him, recognized Paz’s promise and steered him toward an ambitious project analyzing data from NEOWISE. Although the infrared telescope’s chief mission was scanning for near-Earth asteroids, its decade-long observations contain much more.
Mining Years of Unused Astronomical Data
Despite NEOWISE’s retirement, its extensive dataset containing some 200 billion observation entries had valuable records of objects like quasars, variable stars, and eclipsing binaries. These time-variable phenomena had yet to be fully studied due to their complexity.
Where Kirkpatrick initially planned to analyze select sky regions manually, Paz leveraged his skills in advanced mathematics and AI—honed through rigorous courses in Pasadena’s Math Academy—to automate the search.
“His education empowered him to approach the data differently,” the report highlights. Paz, who tackled AP Calculus BC in eighth grade, applied machine learning techniques to identify faint, time-dependent infrared brightness changes, allowing his algorithm to pinpoint candidates swiftly.
Versatile AI Model Reveals Cosmic Treasures
After two years of refinement with professional astronomers, Paz’s system processed the entire NEOWISE archive, uncovering 1.5 million new variable celestial bodies. This expanded dataset sheds light on dynamic cosmic events such as stellar explosions, quasar activity, and binary star interactions, vastly enhancing NEOWISE’s original scientific scope.
“The model I implemented can be used for other time domain studies in astronomy, and potentially anything else that comes in a temporal format,” Paz explained. “I could see some relevance to (stock market) chart analysis, where the information similarly comes in a time series and periodic components can be critical. You could also study atmospheric effects such as pollution, where the periodic seasons and day-night cycles play huge roles.”
Described as a “Submillisecond Fourier and Wavelet-based Model”, this sophisticated method excels at detecting both fast and slow variations often missed by conventional techniques.
Shaping Future Scientists
Following his initial success in 2022, Paz returned in 2024 as a mentor for new high schoolers entering the same academy. Currently employed at Caltech, he collaborates with Kirkpatrick at IPAC, aiding management of data from both NASA and NSF missions.
“Every meeting with Davy is 10% work and 90% us just chatting. It’s been super cool just to have someone to talk to about science like that.”
Kirkpatrick reflected, “When I see talent, I want to nurture it and do all I can to support their growth.”
Paz’s complete catalog of discoveries is slated for public release in 2025, paving the way for new astronomical advancements and demonstrating the power of AI-enhanced research combined with thoughtful mentorship.
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