Most major discoveries in the field of astronomy are associated with domed observatories and experienced researchers. Matteo Paz’s story defies this stereotype at the age of 18, the Pasadena High School student contributed to the discovery of approximately 1.5 million variable objects in space that were unknown to humanity. This discovery was not made by peering through a telescope. It was made by analyzing massive infrared data using mathematics and machine learning algorithms. What began as a research project for a high school student has now revolutionized the way researchers study a constantly moving universe.
Who is Matteo Paz
Matteo Paz is an American high school student whose research has impressed scientists worldwide. He is the son of Amy and Pedro Paz and among his peers, he is recognized not only for his research but also for his leadership qualities. At school, he founded and currently heads a research club for students preparing for science fairs. He was also instrumental in forming the first combined student council for his district and represented students at school board meetings and his background combines hard work in studies with service to the public, which is quite rare for someone of his age.
Matteo Paz Education
Paz attends Pasadena High School and was already advanced in math through the Math Academy program offered by the district. This allowed him to take on linear algebra, statistics and signal processing with ease, concepts that most students wouldn’t see until much later in their education. His inspiration to pursue artificial intelligence came from courses that combined pure math with coding.
Matteo Paz Parents
Matteo Paz’s parents, Amy and Pedro Paz, have maintained a low profile, but their influence is apparent in Paz’s steady academic path. Rather than pointing him toward a single objective, his parents encouraged him to explore disciplines beyond the boundaries of mathematics, computer science and astronomy, not as separate disciplines but as interwoven paths.
How Infrared Data Led Matteo Paz to 1.5 Million Space Discoveries
The NASA’s WISE and NEOWISE missions have been scanning the sky in infrared for more than a decade, accumulating almost 200 billion individual measurements. The scale of this data made it impossible to analyze in the classical way. Paz realized that variability small changes in brightness with time is often indicative of black holes, supernovae or young stars by focusing on the changes rather than the images, Paz tapped into a hidden layer of information that was in plain sight. His work identified approximately 1.9 million variable objects, of which about 1.5 million were uncataloged.
Matteo Paz’s Life Beyond Scientific Research
Despite the breadth of his research, Paz maintains a variety of interests and he tutors younger students through a research club and directs a financial literacy program for middle school students called Money Matters. These activities suggest that his motivation is more than discovery, but also includes developing systems to facilitate learning and participation.
VARnet & the Application of Machine Learning
At the heart of the discovery is VARnet, Paz’s machine learning algorithm developed to interpret infrared light curves. VARnet uses wavelet analysis, signal processing, and deep learning to identify odd patterns that could be missed by human observers. It can analyze each object in a matter of milliseconds using current GPUs after being trained on simulated observations and known parameters. This allowed Paz to analyze the entire NEOWISE single-exposure archive, which was impossible to do before.
What Scientists Found in the New Catalogue
The catalogue that has been created shows a wide range of cosmic events and there are actively feeding supermassive black holes, newly formed stars that are still hidden in clouds of dust and distant stellar explosions that briefly shine before fading away. Varying objects are especially valuable because they show change that indicates motion and evolution rather than static form. Paz’s catalogue offers astronomers a rich map of infrared variability, condensing ten years of raw data into a solid foundation for future discovery.
