UC Irvine doctoral candidates Victoria Knapp-Pérez (left) and Jake Rudolph in the Department of Physics and Astronomy developed the Autonomous Model Builder, or AMBer to explore large, uncharted areas of particle physics theory, helping identify promising new explanations for the behavior of neutrinos.
Photo Credit: Courtesy of University of California, Irvine
Scientific Frontline: Extended "At a Glance" Summary: Autonomous Model Builder (AMBer)
The Core Concept: The Autonomous Model Builder (AMBer) is an artificial intelligence system that autonomously designs theoretical particle physics models to help explain the non-zero mass and behavior of neutrinos.
Key Distinction/Mechanism: Unlike traditional machine learning that identifies patterns in pre-existing data, AMBer utilizes reinforcement learning to learn through trial and error. It constructs models by selecting mathematical symmetry groups, assigning particle behaviors, and evaluating each model's alignment with experimental data while actively minimizing the number of adjustable parameters.
Major Frameworks/Components:
- Reinforcement learning (RL) algorithms designed to autonomously map and explore previously uncharted theoretical spaces.
- Mathematical symmetry groups used to determine and constrain subatomic particle behavior.
- Parameter minimization protocols designed to preserve a theoretical model's predictive power.
- The Standard Model of particle physics, serving as the baseline framework that AMBer seeks to expand upon by addressing its inability to account for neutrino mass.
Branch of Science: Particle Physics, Theoretical Physics, and Artificial Intelligence.
Future Application: Beyond neutrinos, this computational approach can be expanded to efficiently navigate other vast, unexplored theoretical model-building problems across physics, acting as an advanced filtering system that identifies promising mathematical frameworks for human physicists to study.
Why It Matters: Explaining why neutrinos possess mass remains one of the most significant challenges in modern particle physics. By autonomously generating and evaluating new candidate models, AMBer accelerates the discovery of viable physical theories that extend beyond the established limitations of the Standard Model.
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 nonzero 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-Pérez said.
Funding: 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.
Published in journal: Nature Communications Physics
Title: Towards AI-assisted neutrino flavor theory design
Authors: Jason Benjamin Baretz, Max Fieg, Vijay Ganesh, Aishik Ghosh, V. Knapp-Pérez, Jake Rudolph, and Daniel Whiteson
Source/Credit: University of California, Irvine
Edited by: Scientific Frontline
Reference Number: phy071226_01