Latest news with #PlanetFinderAcademy
Yahoo
15-04-2025
- Science
- Yahoo
High Schooler Uncovers 1.5 Million Hidden Objects in Space, Wins $250K
High school student Matteo Paz has stunned the scientific community by identifying 1.5 million previously unknown objects in space. By creating an artificial intelligence algorithm, Paz found a way to sift through vast amounts of space data and uncover millions of cosmic objects hiding in plain sight. His work didn't just impress scientists; it earned him the top prize at the 2025 Regeneron Science Talent Search — a $250,000 award that goes to the most promising young scientists in the U.S. It all started during Caltech's Planet Finder Academy in 2022. The summer outreach program is designed to give high schoolers hands-on experience in astronomy, and Paz was hooked. The following year, he signed up for a six-week Caltech program that pairs students with campus mentors. Paz was assigned to Davy Kirkpatrick. "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," said Paz. "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.' With encouragement from Kirkpatrick, Paz tasked himself with a daunting challenge: How do you process and make sense of nearly 200 terabytes of space data gathered by NASA's NEOWISE (Near-Earth Object Wide-field Infrared Survey Explorer) telescope? NEOWISE has been scanning the sky for asteroids for over a decade. At the same time, it has captured data on other variable objects. According to Caltech, these are 'hard-to-catch phenomena like quasars, exploding stars, and paired stars eclipsing each other.' The NASA telescope was able to detect the varying heat of these objects, but the sheer size of the dataset made it impossible for human astronomers to comb through them. With almost 200 billion rows of data, going through it by hand was never an option, and Paz never considered it. The 18-year-old had a knack for AI, coding, and computer science. Combining this with his substantial math knowledge (he has been studying advanced undergraduate math for the last few years), he created an AI model to sift through the data for him. In the short six-week program, he began using machine learning to make an AI model that could recognize patterns in the infrared data — subtle signals that might indicate the presence of cosmic objects. Kirkpatrick acted as an astronomy consultant and helped him interpret the data. The duo kept working on the project after the six weeks were up. By 2024, Paz was mentoring other high school students on the subject. Now the model can tear through the raw data from the NEOWISE telescope. They have made a jaw-dropping 1.5 million discoveries, each a potential new clue about the structure and history of our universe. Since winning the 2025 Regeneron Science Talent Search, Paz hasn't slowed down. He and Kirkpatrick hope to publish the complete catalog of newly found objects this year. Paz has also secured his first paid job. He is working at Caltech, continuing his research and collaborating with astrophysicists on how to scale his AI system for even bigger projects.
Yahoo
13-04-2025
- Science
- Yahoo
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.
