Accelerating nuclear science with machine learning

The Facility for Rare Isotope Beams, or FRIB, at Michigan State University is home to a world-unique particle accelerator designed to push the boundaries of our understanding of nature.

Now, FRIB is accelerating that work with a form of artificial intelligence known as machine learning with support from the Office of Nuclear Physics, or NP, and the Office of High Energy Physics, or HEP, at the U.S. Department of Energy Office of Science, or DOE-SC.

"Artificial intelligence has the potential to shorten the timeline for experimental discovery in nuclear physics," said Timothy Hallman, DOE associate director of science for Nuclear Physics. "Particle accelerator facilities and nuclear physics instrumentation face a variety of technical challenges in simulations, control, data acquisition and analysis that artificial intelligence holds promise to address."

FRIB scientists have received several grants that aim to bring machine learning's power to process immense data sets to bear in experiments, theoretical studies and the science and engineering that keeps the accelerator humming.

The grants will be led by Christopher Wrede, Dean Lee, Peter Ostroumov, and Yue Hao. All are professors at FRIB and in MSU's Department of Physics and Astronomy in the College of Natural Science. Lee is the head of FRIB's Theoretical Nuclear Science Department, and Ostroumov is the associate director of the FRIB Accelerator Systems Division.

With its grant "Machine Learning for Time Projection Chambers at FRIB," Wrede's team is working to shorten the time to discovery in experiments for nuclear astrophysics, helping better explain processes in stars.

For the grant supporting theoretical work, titled "STREAMLINE Collaboration: Machine Learning for Nuclear Many-Body Systems," Lee and his colleagues are using machine learning in a variety of ways. The common goal is to accelerate the quest for a more robust understanding of the physics at work in the cores of atoms.

The third grant—"Online Autonomous Tuning of the FRIB Accelerator Using Machine Learning"—will have Ostroumov and his team leveraging massive amounts of operations data to help automate facility processes. In doing so, the team is working to minimize upkeep time and help more FRIB users conduct more science.

And Hao is leading two grants: One is titled "Development of Differentiable Beam Dynamics Simulation Tools Including Collective Effects for HEP Accelerator Applications" and the other is "Artificial Intelligence Application in Nonlinear Beam Dynamics Study for Future HEP Accelerators."

Both will help computational studies better integrate and leverage the power of machine learning in accelerator physics. Hao's grants were awarded by the Office of High Energy Physics, while those led by Lee, Wrede and Ostroumov were awarded by the Office of Nuclear Physics, both of which are part of DOE-SC.

FRIB is a DOE-SC user facility serving the global nuclear science research community. The work of these grants will help support that community and the DOE-SC mission to deliver transformative discoveries and scientific tools.

Beyond creating new knowledge and technology, DOE-SC and FRIB are also dedicated to training the next generation of nuclear scientists—an effort that the teams will work to strengthen with these new grants.

"AI and machine learning are hot topics," Lee said. "They're not just useful, they're helping us recruit new students to the field."

In addition to the five grants led by FRIB faculty, another award is supporting a collaboration between FRIB and Virginia State University. Led by Thomas Redpath, an assistant professor in VSU'S Department of Chemistry, the grant also enlists the services of Paul Guèye, associate professor of nuclear physics at FRIB and in MSU's Department of Physics and Astronomy. The team's project is titled "Neural Network Classifier for Analyzing Measurements of Fast Neutrons for Invariant Mass Spectroscopy."

Provided by Michigan State University