Speaker
Description
Jefferson Lab is developing autonomous control systems for polarized cryogenic targets and linearly polarized photon beams, enabling stable, high-performance operation over extended experiment run periods. Historically, maintaining optimal polarization of these critical systems required manual tuning by expert operators. This process is sensitive to experience and prone to human error, and keeps operators focused on low-level system adjustments rather than high-level oversight. Within the AI-Optimized Polarization (AIOP) project, these control systems leverage uncertainty-aware surrogate models and high-fidelity simulation environments to enable reinforcement learning agents to optimize control policies while respecting experimental and operational constraints. For cryogenic targets, surrogate models trained on historical data predict the polarization as a function of microwave frequency, accumulated radiation dose, and electron beam current. For photon beams produced with diamond radiators, simulation environments model the dependence of the photon spectrum and polarization on beam and radiator settings, providing testbeds for optimization strategies. We will present the surrogate- and simulation-based environments used for training, show the improved performance for polarized cryotargets, implementation strategies for both use cases, and a road map toward operations in which autonomous systems control routine shift operations while humans focus on oversight, safety, and scientific objectives.