Agnieszka Sorensen receives NSF CAREER Award to study dense nuclear matter

Agnieszka Sorensen, assistant professor of physics at the Facility for Rare Isotope Beams (FRIB) and in the Michigan State University (MSU) Department of Physics and Astronomy, has received a Faculty Early Career Development Program (CAREER) award from the U.S. National Science Foundation (NSF).

The award will support Sorensen’s project, “CAREER: Microscopic Transport, Machine Learning, and Bayesian Analysis for Extracting the Dense Nuclear Matter Equation of State from Heavy-Ion Collisions at RHIC and FRIB.”

Sorensen will combine advanced theoretical modeling, machine learning, and statistical methods with data from heavy-ion collision experiments at FRIB and the Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Laboratory. Her goal is to map out how dense nuclear matter behaves under varying conditions of density, temperature, and composition.

“Receiving the NSF CAREER Award is an enormous boost for the research program I pursue at FRIB and MSU,” Sorensen said. “One of the most exciting aspects of this project is its potential to spur collaborations with members of the nuclear physics community and with researchers using similar techniques across various disciplines.”

Studying matter under extreme conditions

Experiments at FRIB and RHIC collide heavy nuclei at high speeds, temporarily creating dense nuclear matter—systems of particles interacting through the universe’s strong force. By using computer simulations to understand how these systems evolve and produce the particles measured by detectors, researchers can learn how the strong force behaves in many-body systems.

This behavior is governed by the nuclear matter equation of state, a mathematical function which allows one to calculate how pressure and energy change across varying temperature, density, and neutron-proton abundance.

Theoretical calculations for these systems are not only extremely complex, but in many cases, also entirely inaccessible within existing approaches, leaving these relationships unknown across vast ranges of conditions.

“The equation of state is a fundamental property of nuclear matter that can tell us about its behavior in different environments,” Sorensen said. “Experiments at RHIC and FRIB, together with simulations, can help us determine how nuclear matter behaves under different conditions. This can deepen our understanding of the strong force and help test theoretical predictions.”

The results will also inform scientists’ understanding of neutron stars and neutron star mergers, where matter exists at extreme densities.

Using machine learning to connect theory and experiment

Machine learning will enable Sorensen and her research group to conduct the large-scale statistical analyses needed to compare complex computer simulations with experimental measurements.

Sorensen’s group will develop a framework to vary the equation of state used in simulations and measure how those variations affect predicted outcomes. Statistically identifying which equation of state best describes experimental data typically requires comparisons against simulations representing hundreds of thousands of possibilities.

Running that many full simulations is not practical, even with high-performance computing resources. Instead, Sorensen will use a smaller subset of simulations to train a machine-learning emulator. She will then use statistical methods to determine which descriptions of nuclear matter best describe the experimental data.

“Machine learning is key to making this analysis possible,” Sorensen said. “The emulator will allow us to make the hundreds of thousands of comparisons we need without having to run a full simulation each time.”

Sorensen noted that FRIB and MSU provide cross-disciplinary expertise for the project. She is collaborating with FRIB experimentalists on methods for analyzing heavy-ion collision data, as well as drawing on the expertise of nuclear theorists who model strong interactions and specialize in machine learning for nuclear science.

“Agnieszka’s award recognizes a compelling research program aimed at answering fundamental questions about dense nuclear matter,” said FRIB Laboratory Director Thomas Glasmacher. “Her work strengthens connections among theory, experiment, and computational science. This is an important program that will gain impact on the experimental side with FRIB400, the energy upgrade of the FRIB linear accelerator that was articulated in the FRIB400 whitepaper.”

Sorensen received a PhD in physics from the University of California, Los Angeles, where her research centered on heavy-ion collisions. She completed most of her doctoral research at Lawrence Berkeley National Laboratory. She then held a postdoctoral position at the Institute for Nuclear Theory at the University of Washington before joining FRIB in 2024.

Michigan State University operates the Facility for Rare Isotope Beams (FRIB) as a user facility for the U.S. Department of Energy Office of Science (DOE-SC), with financial support from and furthering the mission of the DOE-SC Office of Nuclear Physics. Hosting what is designed to be the most powerful heavy-ion accelerator, FRIB enables scientists to make discoveries about the properties of rare isotopes in order to better understand the physics of nuclei, nuclear astrophysics, fundamental interactions, and applications for society, including in medicine, homeland security, and industry.

The U.S. Department of Energy Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of today’s most pressing challenges. For more information, visit energy.gov/science.

For FRIB news, visit frib.msu.edu/news.