AI system developed by UC Irvine physicists helps explain why neutrinos have mass

Irvine, Calif., July 9, 2026 —Physicists at the University of California, Irvine have developed an artificial intelligence system that can autonomously design theoretical physics models, a task traditionally carried out by human theorists. The approach allows researchers to explore large, uncharted areas of particle physics theory, helping identify promising new explanations for the behavior of neutrinos.

The system is called Autonomous Model Builder, or AMBer, and was developed by a research team led by UC Irvine doctoral candidates Victoria Knapp- Pérez and Jake Rudolph in the Department of Physics and Astronomy. The work is described in a study published in Nature Communications Physics.

AMBer uses reinforcement learning, a form of artificial intelligence that learns through trial and error rather than by following predefined instructions. As it explores possible particle physics theories, the system evaluates its own choices and improves over time.

“Reinforcement learning is different from other kinds of machine learning, in which models predict labels or find patterns in data,” Rudolph said. “AMBer’s RL framework allows it to learn about the space of theoretical models as it explores, effectively creating its own training data as it searches for promising models.”

The system constructs particle physics models by selecting mathematical symmetry groups, determining which particles to include and assigning how those particles behave under the chosen symmetries. Each proposed model is evaluated based on how well it matches experimental data while minimizing the number of adjustable parameters, a key measure of a theory’s predictive power.

The researchers tested AMBer on well-studied classes of neutrino theories and demonstrated that it could reproduce known results. They then applied the system to previously unexplored mathematical frameworks, identifying new candidate models that may merit further investigation.

Neutrinos are subatomic particles with extremely small but non-zero mass – a property not explained by the Standard Model of particle physics. Developing theories that explain neutrino mass remains one of the field’s major challenges.

The researchers emphasized that the system is designed to assist, not replace, human physicists by narrowing vast theory spaces down to the most promising candidates.

“AMBer functions as a filter, giving human physicists a better-informed starting point from which to study more complex behavior of neutrino models,” Knapp-Perez said.

Additional collaborators include Max Fieg, a former UC Irvine doctoral student now a postdoctoral fellow at Fermilab; Aishik Ghosh, a former UC Irvine postdoctoral scholar now a professor at the Georgia Institute of Technology; and Daniel Whiteson, UC Irvine professor of physics, who supervised the research. Jason Baretz, UC Irvine doctoral student in Whiteson’s group, also contributed to the research.

This research used computing resources from the National Energy Research Scientific Computing Center. Funding was provided in part by the National Science Foundation, UC-MEXUS-CONACyT and the Department of Energy’s Office of High Energy Physics.

About the University of California, Irvine: Founded in 1965, UC Irvine is a member of the prestigious Association of American Universities and is ranked among the nation’s top 10 public universities by U.S. News & World Report. The campus has produced five Nobel laureates and is known for its academic achievement, premier research, innovation and anteater mascot. Led by Chancellor Howard Gillman, UC Irvine has more than 36,000 students and offers 224 degree programs. It’s located in one of the world’s safest and most economically vibrant communities and is Orange County’s second-largest employer, contributing $7 billion annually to the local economy and $8 billion statewide. For more on UC Irvine, visit www.uci.edu.

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