Spring, 2025
Dr. Ivana Damjanov studies the relationship between galaxy size growth, changes in galaxies’ stellar and dark matter content and their position in the universe.
“We are interested in the connection between the evolution of galaxies and their surroundings as cosmic time goes on,” says Damjanov, associate professor at St. Mary’s University. “Galaxies can live either surrounded by basically nothing or they can be in very dense regions of the universe called galaxy clusters.”
She and her team zoom in on galaxies to see how they change their appearance in telescope images.
“We take the images of millions of galaxies and we quantify their appearance,” she says. “We measure their size, shape, how squished they are and we compare that to how many stars they’re making, how fast these stars are moving and what they are surrounded by. Our goal is to understand how the changes galaxies are going through work together and which physical processes drive those changes.”
While answering these fundamental questions, Damjanov and her team are also developing techniques that can be used in everyday life. The techniques she’s using to analyze her large-scale images are advanced statistical methods and the instruments they’ve been developing are used, for example, in medical imaging.
Dr. Michele Pizzardo, Damjanov’s postdoctoral fellow, is using ACENET to see how simulated clusters of galaxies evolve and grow.
“We are interested in the study of these galaxy clusters,” Pizzardo says. “I’m interested in the external part of these structures. This part is sensitive to the basic laws the universe follows. It can give us a way to test different cosmological models to determine whether a model is viable.”
Using ACENET, he developed a “recipe” to understand how much these clusters are accreting at different ages of the universe.
“We need a lot of clusters and then we use all the information — the temperature, entropy, energy information, position of each particle in these boxes,” he says. “We do this at different ages of the universe. One dataset can be 40 to 50 terabytes of data.”
Obviously, he had to use ACENET’s high performance computing technology to do these calculations. Damjanov adds that other members of her team also use ACENET to analyze actual images from telescopes.
“In principle, our software could be run on a standard computer,” Pizzardo says. “ However, the size of the data that our software processes makes it impossible to obtain any results. In other words, if the data we had to analyze were a few GBs, we could run the software on a local machine, but our data is more than 1,000 times larger than that so the use of a cluster infrastructure is essential.”
He says the team members’ models are computationally demanding because their random access memory needs can climb up to 100 gigabytes and they need dozens of high-performance nodes to obtain results in a reasonable amount of time. Storage needs are another consideration.
In other projects, Damjanov’s students are creating simulated galaxies and adding them to the existing telescope images, thus bringing together voluminous simulated and real-world datasets. ACENET is essential for that big-data-driven work.
