UNH Physicist Receives DOE Funding for AI Research in Nuclear Physics
Nathaly Santiesteban, an assistant professor of physics and astronomy at UNH, has been awarded funding from the Department of Energy’s Genesis Mission. This prestigious program, known for its competitiveness, aims to leverage artificial intelligence (AI) to enhance the understanding of spin and the fundamental structure of matter, addressing critical inquiries in modern nuclear physics.
Santiesteban’s project is among only 278 selected from over 5,000 proposals submitted to the Genesis Mission, which seeks to enhance scientific productivity in the U.S. through AI-driven methodologies.
“This research addresses a fundamental question: How does matter acquire the properties we observe? Most visible matter is made of protons and neutrons, yet we still do not fully understand how their internal components produce properties such as spin,” explains Santiesteban.
The project will focus on improving polarized-target systems, which are specialized setups used in nuclear and particle physics. These systems align the spin axes of atomic nuclei and will be made more efficient, reliable, and responsive through AI. Collaborating alongside Santiesteban are UNH co-principal investigators Karl Slifer and Elena Long, utilizing the polarized-target laboratory at UNH as their primary site for testing.
The funding includes $750,000 for the initial nine-month phase, with the potential for up to $15 million in a subsequent three-year phase. Santiesteban will work with researchers from the Department of Energy’s Jefferson Lab and several universities, including the University of Virginia, the University of Tennessee, and New Mexico State University.
Polarized-target systems are notably complex, operating at temperatures below those found in outer space. They require powerful magnetic fields, microwaves, and sophisticated radio-frequency equipment—all under strict safety protocols. Scientists utilize these systems to direct particle beams into polarized materials, analyzing how the particles scatter.
Santiesteban highlights the broader implications of the project, stating, “This project could also demonstrate how AI can work safely with complex scientific equipment, potentially benefiting other laboratories and advanced technologies. Running these experiments is demanding; every part of the system must be meticulously monitored and controlled to achieve the desired polarization. Our goal is to minimize manual intervention and enhance experimental efficiency using AI for real-time monitoring and optimization.”
