🤖 AI Summary
Traditional accelerator tuning relies on manually crafted procedures, which are difficult to reuse efficiently after lattice modifications, thereby hindering rapid early-stage design iteration. This work proposes the first end-to-end autonomous algorithm discovery framework driven by a language model agent, integrating large language models, a particle accelerator simulation platform, and an automated feedback loop to iteratively generate and refine tuning algorithms starting from minimal initial code. For the first time, an intelligent agent directly participates in exploring accelerator tuning strategies, successfully producing 16 non-dominated algorithms in the ALS-U accumulator ring model. These algorithms exhibit diverse physical trade-offs between beam capture speed and error correction performance and significantly outperform expert-designed solutions.
📝 Abstract
Simulated commissioning has become essential for de-risking modern light-source design and commissioning, but the procedures being simulated are still designed entirely by human experts. Their labor-intensive redevelopment after lattice changes makes such studies hard to repeat and limits their use during early design iteration. This Letter demonstrates a closed research loop in which a language-model agent writes commissioning code, tests it in simulation, and improves the algorithm from the results. Applied to RF beam capture in the ALS-U accumulator-ring model, the loop substantially improves a working expert procedure and can construct a working one from a minimal starting point, with more capable models succeeding from less initial code. Extending the same framework to multiple objectives produces 16 non-dominated algorithms spanning physically distinct trade-offs between rapid beam capture and correction of seeded machine errors. This reframes commissioning studies from evaluating human-designed procedures toward a mode in which agents participate directly in discovering accelerator algorithms.