Feature Stories 2026: Tyler Carlson '25, '26: Machine learning among the stars

While most of the UMass Dartmouth campus sleeps, physics student Tyler Carlson is wide awake under the red LED glow of the observatory dome. Peering into a computerized telescope, he searches for signs of planets outside our solar system. 

"As a kid, I wanted to go to space," he said. "Now, my life goal is to contribute to our understanding of the universe." A NASA Massachusetts Space Grant recipient, Carlson was the first student to conduct research at UMass Dartmouth's Hirshfeld-Dowd Observatory since its grand reopening in 2023. With a combination of data science and astrophysics called astro-informatics, Carlson is training a neural network that can classify potential exoplanets using data from major space telescopes. 

Why UMass Dartmouth? 

Carlson applied to several large universities but was drawn to UMass Dartmouth's student-faculty ratio and the close-knit nature of the physics department. "I knew the small class sizes would give me more of a personal experience," he said, "but I wasn't expecting to become so close with my professors. I'm on a first name basis with my teachers, and they all care about their students as individuals, not just about our work." 

UMass Dartmouth also offered Carlson the flexibility to pursue both astrophysics and computational physics, combining his curiosity about space with new possibilities in machine learning. 

"Here, everybody is rooting for you. If you make the effort, people will help you get where you want to go. It's not about how much you know, it's about how much you're willing to learn."

Using machine learning to study the universe 

"There are hundreds of billions of stars just in our galaxy and relatively few astronomers. Access to observatories can be expensive, and analyzing data from observations is time-consuming. This is where machine learning comes in," Carlson explained.  

Using exoplanet candidate lists from space telescopes like Hubble and Webb, Carlson built a neural network that can learn patterns in the data and classify potential exoplanets with about 90% accuracy. His long-term goal is to help observatories use these predictions to prioritize targets and make their time more efficient. 

He hopes to eventually run his model on data collected right here on campus. 

"I’ve just scratched the surface of what neural networks can do with time-series data," he said. "There are so many applications beyond exoplanets. Space is full of data, and neural networks thrive on data." 

Hands-on discovery under the dome 

Although his official research centers on machine learning, Carlson says his proudest work has happened in the observatory. "Burning the midnight oil up there—it’s where I feel most connected to the work," he said. "Projects I’ve done at the observatory include narrowing parameters of an eclipsing binary star system and a study testing whether smart telescopes can be used for research by comparing their results to professional-grade observatory equipment." 

In 2024, Carlson received two NASA Massachusetts Space Grants: one supporting his machine learning project and another backing hands-on observational work. As the first student researcher in the reopened observatory, Carlson helped advocate for new equipment, involve the Society of Physics Students with observatory events, and open the space to the community. He’s helped lead programs for Dartmouth High School students and hosted local Scout troops to encourage their interest in STEM. 

Supporters and mentors 

Carlson credits several faculty members with shaping his academic experience. "Dr. Renuka Rajapakse has been my advisor for three years, and she's been a great source of support. Dr. Sarah Caudill's work involves gravitational waves and neural networks, and she's helped me deepen my understanding of time-series data."  

He also points to Mark Munkacsy, director of the campus observatory and president of the Astronomical Society of Southern New England: "He’s incredibly knowledgeable and always willing to help." 

Any advice for younger students? 

"Talk to your professors, ask questions until you understand, and don't give up. Here, everybody is rooting for you. If you make the effort, people will help you get where you want to go. 

"It's not about how much you know, it's about how much you're willing to learn."