Speakers
Description
Radio-frequency (RF) bunch-merging gymnastics is used in the RHIC heavy-ion program to combine individual source pulses into single bunches with suitable intensity. Preserving intensity and emittance during these gymnastics requires careful coordination of the voltages and phases of RF cavities at several harmonic numbers, which is labor-intensive and fragile against machine drift. Recent work using a physics-based simulator of the Brookhaven Alternating Gradient Synchrotron (AGS) has shown that reinforcement learning (RL) can learn effective merge configurations. RL is data-intensive and requires many training interactions with the environment. Large language models (LLMs) have recently demonstrated the ability to extract patterns from large, noisy data and to integrate domain knowledge into the control loop, making them an attractive aid for tuning complex accelerator systems. However, domain adaptation (i.e., prompt engineering, finetuning, etc.) is always required for deploying LLM in the target domain and has not been investigated in particle accelerators. To fill this gap, we propose an active supervision framework in which the LLM-based teacher first transfers general control principles from human operators to the student agent. Then, the student agent further finetunes the control policy by interacting with the simulator/experiments with improved sample efficiency.
Funding Agency
DE-SC0024287 and DE-SC0025351
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