Steward PhD Alum Mentors Teen Who Wins $250K for Using AI to Discover 1.5 Million Hidden Objects in Space

High school senior Matteo Paz stunned the astronomy world by uncovering 1.5 million previously unknown cosmic objects using a machine-learning model he developed at Caltech.

What started as a summer research program transformed into a groundbreaking scientific contribution, earning him a $250,000 science prize and a first-author paper.

“I’m so lucky to have met [Steward Phd Alum and Astronomy Camp counselor] Davy Kirkpatrick,” Paz says. “He has allowed an unbridled learning experience. I think that’s why I’ve grown so much as a scientist.”

Teen Prodigy Discovers 1.5 Million New Cosmic Objects

While conducting research at Caltech, local high school student Matteo (Matthew) Paz discovered 1.5 million previously unknown objects in space, expanded the scientific potential of a NASAmission, and published a peer-reviewed, single-author paper.

His work, detailed in an article in The Astronomical Journal, describes an AI algorithm he developed to analyze archival data from a retired NASA space telescope. The algorithm not only led to the discovery of new celestial objects but can also be used by other astronomers and astrophysicists to study similar data.

For this groundbreaking research, Paz, now a senior at Pasadena High School, won first place and a $250,000 award in the prestigious Regeneron Science Talent Search, a national competition run by the Society for Science.

A Lifelong Fascination Sparked Early

Paz’s interest in astronomy began in grade school, when his mother took him to public Stargazing Lectures at Caltech. In 2022, he joined the Caltech Planet Finder Academy, a summer program led by astronomy professor Andrew Howard. The following year, he enrolled in Caltech’s six-week Summer Research Connection, a program that pairs local high school students with research mentors in campus labs.

2025 Regeneron Science Talent Search award finalists stand on risers as Matteo Paz, back row, looks shocked at his first-place win. Credit: Society for Science

Mentorship That Fuels Scientific Growth

Astronomer and IPAC senior scientist Davy Kirkpatrick served as Paz’s mentor. Kirkpatrick has mentored high school students for the last five summers, in addition to an undergraduate student, citizen scientists, and visiting graduate fellows.

“I’m so lucky to have met Davy,” Paz says. “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.’ He has allowed an unbridled learning experience. I think that’s why I’ve grown so much as a scientist.”

Kirkpatrick grew up in a farming community in Tennessee and realized his dream of becoming an astronomer with the help of his ninth-grade chemistry and physics teacher, Marilyn Morrison. She told him and his mother that he had potential and explained what courses he should take to prepare for college.

“I wanted to pass on that same sort of mentoring to someone else and hopefully many someone elses,” Kirkpatrick says. “If I see their potential, I want to make sure that they are reaching it. I’ll do whatever I can to help them out.”

This artist’s concept shows the Wide-field Infrared Survey Explorer, or WISE, spacecraft, in its orbit around Earth. In its NEOWISE mission it finds and characterizes asteroids. Credits: NASA/JPL-Caltech

NEOWISE: A Goldmine of Untapped Cosmic Data

Kirkpatrick also wanted to glean more insight from NEOWISE (Near-Earth Object Wide-field Infrared Survey Explorer), a now-retired infrared telescope that had scanned the entire sky in search of asteroids and other objects near Earth for more than 10 years. While the NASA telescope was busy observing asteroids, it also detected the varying heat of other more distant, cosmic objects that flashed intensely, pulsated, or dimmed as they were eclipsed. Astronomers call these variable objects: hard-to-catch phenomena like quasars, exploding stars, and paired stars eclipsing each other. But the data on these variable objects had not yet been harnessed. If the NEOWISE team could identify those objects and make them available to the astronomical community, the resulting catalog could provide insight into how the cosmic entities change over years.

“At that point, we were creeping up towards 200 billion rows in the table of every single detection that we had made over the course of over a decade,” Kirkpatrick says. “So my idea for the summer was to take a little piece of the sky and see if we could find some variable stars. Then we could highlight those to the astronomic community, saying, ‘Here’s some new stuff we discovered by hand; just imagine what the potential is in the dataset.'”

Matteo Paz with Caltech President Thomas F. Rosenbaum at the Regeneron Science Talent Search award ceremony on March 11, 2025. Rosenbaum, the Sonja and William Davidow Presidential Chair and professor of physics, gave an address at the event as the Society for Science board chair and a Science Talent Search alumnus. Credit: Society for Science

Revolutionizing Astronomy with Machine Learning

Paz had no intention of sifting through the data manually. His schoolwork had prepared him to bring a new viewpoint to the challenge. He’d taken an interest in AI during an elective that integrated coding, theoretical computer science, and formal mathematics.

Paz knew that AI trains best on vast, orderly datasets like the one Kirkpatrick had given him. And Paz had the advanced math knowledge that he needed to enjoy programming: He was already studying advanced undergraduate math in Pasadena Unified School District’s Math Academy, in which students finish AP calculus BC in eighth grade.

So Paz set off to develop a machine-learning technique to analyze the entire dataset and flag potential variable objects. In those six weeks, he began to draft the AI model, which began to show some promise. As he worked, he consulted with Kirkpatrick to learn the relevant astronomy and astrophysics.

“Every meeting with Davy is 10 percent work and 90 percent us just chatting,” Paz says. “It’s been super cool just to have someone to talk to about science like that.”

Kirkpatrick also connected Paz with Caltech astronomers Shoubaneh Hemmati, Daniel Masters, Ashish Mahabal, and Matthew Graham, who shared their expertise in machine-learning techniques for astronomy and in the study of objects that vary on short and long timescales. Paz and Kirkpatrick learned that the particular rhythm of NEOWISE’s observations meant that it would be unable to systematically detect and classify many objects that either flashed once quickly or changed gradually over a long time.

From Student to Teacher and Collaborator

As the summer concluded, there was still plenty to do. In 2024, Paz and Kirkpatrick again collaborated, and this time, Paz mentored other high school students.

Now, Paz has refined the AI model to process all of the raw data from NEOWISE’s observations and has analyzed the results. Trained to detect minute differences in the telescope’s infrared measurements, the algorithms flagged and classified 1.5 million potential new objects in the data. In 2025, Paz and Kirkpatrick plan to publish the complete catalog of objects that varied considerably in brightness in the NEOWISE data.

Matteo Paz presents the initial work on his project at a seminar in 2023. Credit: Kitty Cahalan

AI’s Wider Impact Beyond Space

“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 says. “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.”

Paz’s Regeneron Science Talent Search experience taught Kirkpatrick something about mentoring. “When they announced Matteo was the winner of the science competition, that was the highest high I’ve ever had,” he says. “I’ve won awards in the past as well, and that’s a big thrill, but when you’ve helped someone reach some of their potential and be acknowledged for it, it’s a nice feeling.”

Kirkpatrick adds: “The extent to which we can tap into the local community of really smart young people, mentor them, and make sure they don’t forget and lose their potential, the better off we are.”

From Passion Project to First Job

Now, while he finishes high school, Paz is a Caltech employee. He works for Kirkpatrick in IPAC, which manages, processes, archives, and analyzes data from NEOWISE and several other NASA and NSF–supported space missions. It’s Paz’s first paying job.

Reference: “A Submillisecond Fourier and Wavelet-based Model to Extract Variable Candidates from the NEOWISE Single-exposure Database” by and Matthew Paz, 7 November 2024, The Astronomical Journal.
DOI: 10.3847/1538-3881/ad7fe6