High school student decodes NASA data, maps 1.5 million unidentified space objects

A summer project for a teenager while working on archival data from NASA has discovered 1.5 million previously unidentified objects. This chilling revelation came last year only when Matteo Paz, who is from California, is also a student at Pasadena High School. He joined Planet Finder Academy in the summer of 2022. The program immerses students in real world astronomy challenges.

He won over $250,000 for the discovery of 1.5 million unidentified objects.

The discovery was made by Matteo Paz, a high school student from California, while analysing archival data from NASA during the summer of 2022. Paz was part of the Planet Finder Academy, a programme that introduces students to real-world problems in astronomy and data science.

Under the guidance of Davy Kirkpatrick, a scientist at the Infrared Processing and Analysis Centre (IPAC), Paz worked with data collected by NASA’s NEOWISE mission.

FROM ARCHIVAL DATA TO NEW COSMIC DISCOVERIES

Launched in 2009, NEOWISE was designed to track near-Earth asteroids. Over a decade, it scanned the entire sky in infrared light, creating an archive of nearly 200 billion individual measurements.

The dataset included signals from stars, galaxies, and distant cosmic events, many of which had never been examined in detail.

The original plan was to study a small portion of the archive manually. Paz, however, took a different approach. Using his background in mathematics and coding, he developed an automated system to analyse the data at scale.

Over six weeks, he built a machine-learning pipeline capable of detecting objects whose brightness changes over time. These variations are often subtle and irregular, making them difficult to identify through traditional methods.

“The model started showing promise almost right away,” Kirkpatrick told Phys.org. As the system was refined, it began identifying objects that flicker, pulse, or slowly fade, patterns linked to quasars, binary star systems, supernovae, and other variable sources.

To achieve this, Paz applied mathematical techniques such as Fourier transforms and wavelet analysis.

These tools allowed the system to detect faint changes in infrared light that had been overlooked in earlier studies. Some objects varied too slowly, or too briefly, to be noticed during initial scans.

Such variable sources are important for astronomers because they help explain rare and short-lived cosmic events that do not follow fixed patterns. Studying them can offer insights into stellar evolution and extreme astrophysical processes.

Paz worked alongside researchers at California Institute of Technology, including Shoubaneh Hemmati, Daniel Masters, Ashish Mahabal, and Matthew Graham. Together, they expanded the system to process the full dataset.

The result was a new catalogue containing more than 1.5 million previously unidentified variable objects across the sky.

The finding underscores the growing role of data science in astronomy and shows how large scientific archives, when revisited with new tools, can still yield major discoveries — sometimes from unexpected places, including a summer project by a high school student.