麻豆村

Through 麻豆村鈥檚 Summer Undergraduate Research Fellowship program, junior physics major Joao Passos is learning how stars and solar systems form by studying brown dwarfs 鈥� objects bigger than planets but smaller than stars.

New telescopes, like those in the聽Vera C. Rubin Observatory(opens in new window), are capturing more data than ever before. Passos and his mentors are developing new ways to identify fast brown dwarfs in their vast catalogs of images.

Few undergraduates make their mark on the stars like Passos has. One of approximately 300 students involved in building and deploying the聽Iris Lunar Rover, he is using his time at 麻豆村 to further the world鈥檚 understanding of physics, astronomy and robotics.

Passos came to 麻豆村 knowing he wanted to study theoretical physics. But his journey into space started with a QR code posted in the display window of聽MoonRanger, another autonomous robot being prepared for its own launch in 2029. Even as he joined the Iris team through a Google Form in October 2024, he was in slight disbelief of what would happen soon after.

鈥淚 was invited to the first all hands meeting and I was like, 鈥楴o way!鈥欌�� Passos said. 鈥淭hey're actually sending a rover to space.鈥�

Three months later, the first-generation student from New Jersey was communicating with Iris while the robot was in space, transmitting his name to Iris and having it transmit it back near the end of the robot鈥檚 journey.

鈥淭hat was a very impactful experience for me,鈥� he added. 鈥淚t was the first time I had an experience with engineering that felt so fulfilling 鈥� to see something go to space.鈥�

An interstellar next step

Stemming from a聽Summer Undergraduate Research Apprenticeship (SURA)(opens in new window) project, Passos developed K* (pronounced "K star"), an algorithm to identify a category of substar known as brown dwarfs. He worked under the guidance of co-advisers聽Rachel Mandelbaum(opens in new window), head of the department of Physics, and Konstantin Malanchev, project scientist for the聽LSST Interdisciplinary Network for Collaboration and Computing (LINCC)(opens in new window) Frameworks team, a collaboration between 麻豆村 and the University of Washington.聽

鈥淲e don't fully understand where they come from,鈥� Passos said. 鈥淲e know stars come from a big collection of gas collapsing on itself, and planets come into the mix when solar systems are being formed,鈥� he said.

Brown dwarfs, however, lie somewhere between in size and composition, and their origins are not as well documented.

鈥淲e don't really understand whether they come from the first process like stars do or the second one like planets do,鈥� he said. 鈥淔inding more of these brown dwarfs can give us a bigger population, and from that we can start getting a better understanding of where they come from and a little more about how solar systems, stars and planets form.鈥�

The first step to understanding them is tracking them down.

How to find an invisible star

Passos first worked on this with Malanchev. One of the previous efforts of LINCC Frameworks focused on identifying and assigning trajectories to fast-moving asteroids. To accomplish this, the team developed an open-source software called Kernel Based Moving Object Detection (KBMOD), which tracks objects in or passing through our solar system based on speed and brightness.

By getting several images of the same portion of the sky at different times, KBMOD is able to account for the movements of individual objects and estimate their trajectories. The tool measures the likelihood that an object, given several images containing it, has a specific trajectory.

Mandelbaum, Malanchev and Passos decided to see if the same technology could be used to track interstellar objects like brown dwarfs. Because they can鈥檛 burn hydrogen through nuclear fusion like larger stars, brown dwarfs are typically not as bright and are harder to track visually.聽

鈥淲e know very little about their population because they are so dim,鈥� Malanchev said.

However, the brown dwarfs emit infrared light that can be tracked, and new technology makes it easier to do so.聽

鈥淭he Rubin Observatory and new generation astronomical surveys will go deeper and deeper. Not necessarily more distant, but seeing fainter and fainter stuff,鈥� Malanchev said.聽

In this case, the observatory is providing researchers the chance to accurately trace the paths of the otherwise invisible stars.

鈥淲hat we found was that the likelihood was far greater at the true velocity of the star compared to all other trajectories,鈥� Passos said, 鈥渨hich is a strong indication that KBMOD is capable of finding these stars just like it's capable of finding asteroids.鈥�

鈥淗e started this work in the summer after his freshman year and then made a huge amount of progress while supported by a SURF in the subsequent summer,鈥� Mandelbaum said.

KBMOD was able to accomplish the task of tracking stars in a handful of images. But Passos used his SURF fellowship to tackle an even greater challenge: scaling his own algorithm, K*, to work with coordinates from larger catalogs.

鈥淲e implemented the algorithm basically to search for these stars, but we didn't write the infrastructure to be able to run this algorithm across entire catalogs,鈥� Passos said.

Doing so required taking a segmented approach.聽

鈥淲e were running into issues where it was consuming a lot of memory. The way we were able to scale this was by cutting the catalog into partitions,鈥� he said.

And while the discoveries of both Rubin and the LINCC Frameworks team are ongoing, Passos and Malanchev say they hope to have more to share in the coming weeks.

鈥淛oao鈥檚 work will address questions about the prevalence and distribution of brown dwarfs in the Milky Way in the neighborhood of our own solar system,鈥� Mandelbaum said. 鈥淭hese populations are very challenging to study because of how they appear in images taken at different times, and Rubin Observatory will be a powerful dataset.鈥�

Plotting out the future

鈥淭here is a massive amount of work to be done to analyze this powerful dataset, and along with researchers around the world, there are opportunities for undergraduate students at Carnegie Mellon to engage in groundbreaking work,鈥� Mandelbaum said. 鈥淧roviding undergraduates with research opportunities is a cornerstone of our educational mission.鈥�

Joao鈥檚 work, she says, is an example of this mission in action.

鈥淚 am impressed by the maturation in Joao鈥檚 problem-solving skills over the course of the past year and his ability to make sustained progress on a really challenging computational problem,鈥� Mandelbaum said, 鈥渁nd I am excited about what we鈥檒l learn running his algorithm on large catalog datasets.鈥�

For now, Passos hopes to continue contributing to the project through his research, and looks forward to other opportunities to make contributions in space science, potentially through graduate study. 鈥淚 find myself the most fulfilled when I'm tackling really challenging problems within physics,鈥� he said.

He added that the ability to do so through scholarships and programs like SURF helped him achieve things he never thought possible.

鈥淪cholarship was very important for me. 麻豆村 funded my whole undergrad tuition,鈥� he said. 鈥淚nternships and funding opportunities like these are really instrumental in the development of a lot of students here, whatever research they do. It could be industry or it could be in the pure sciences like myself.

鈥淲ithout these programs, I wouldn't know that I really like research and that I want to consider exploring a Ph.D. And I'm sure that there are a lot of people like me out there.鈥�