US high school student's AI identifies 1.5 million previously unknown space objects
A high school student from the US has discovered a whopping 1.5 million cosmic objects in space, which were previously unknown, using AI. Matteo (Matthew) Paz developed a new AI algorithm identify the objects while undertaking a research project as part of the Planet Finder Academy outreach program offered at California Institute of Technology (Caltech).
Paz's article in The Astronomical Journal outlines the AI algorithm he developed, which can be used by other researchers. His interest in astronomy began in grade school, inspired by public stargazing lectures his mother took him to at Caltech.
In the summer of 2022, Paz joined the Planet Finder Academy led by professor of astronomy Andrew Howard, where he studied astronomy and computer science. During the program, he was mentored by Caltech's Infrared Processing and Analysis Center senior scientist Davy Kirkpatrick, whose guidance helped Paz take on an ambitious research project that ultimately led to a published paper.
Kirkpatrick, who grew up in a farming town in Tennessee, was inspired to pursue astronomy thanks to a supportive high school science teacher who encouraged his potential and helped him plan for college.
The scientist was motivated to offer the same kind of mentorship that had once shaped his own path, helping young researchers realize their potential. Kirkpatrick also saw an opportunity to extract deeper insights from Near-Earth Object Wide-field Infrared Survey Explorer (NEOWISE), the retired infrared telescope that spent over a decade scanning the sky for asteroids and other near-Earth objects.
Beyond tracking asteroids, NASA's NEOWISE telescope also recorded heat signals from distant cosmic objects that brightened, dimmed, or pulsed—phenomena known as variables, including quasars, exploding stars, and eclipsing binaries. Much of this data remained untapped. By identifying these objects, researchers could build a catalog that sheds light on how such celestial phenomena change over time.
With Paz on board, the approach quickly shifted. Instead of combing through the data manually, Paz applied his background in AI—shaped by an elective combining coding, theoretical computer science, and formal math—to Kirkpatrick's study. Trained through Pasadena Unified's Math Academy, where students reach AP Calculus BC by eighth grade, Paz had the skills to turn NEOWISE's massive dataset into a training ground for machine learning.
Determined to tackle the challenge at hand, Paz developed a machine-learning technique to analyze the entire NEOWISE dataset and flag potential variable objects. In just six weeks, he drafted an AI model that showed promise. Along the way, he consulted with Kirkpatrick to gain insights into the relevant astronomy and astrophysics.
Last year, Paz and Kirkpatrick reunited to continue their research. With Paz now mentoring other high school students, the young researcher has refined the AI model to process the raw NEOWISE data and analyze the results. The model, trained to detect subtle variations in the telescope's infrared measurements, identified and classified 1.5 million potential new objects. In 2025, Paz and Kirkpatrick plan to publish a comprehensive catalog of objects that showed significant brightness changes in the NEOWISE data.
According to Paz, the model he developed can be applied to time-domain studies in astronomy and any field involving temporal data. "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, now an employee at Caltech, added.
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