Reinforcement learning optimizes nuclear physics experiments

Researchers have developed a novel data-driven control framework that leverages reinforcement learning and surrogate modeling to optimize target polarization in nuclear physics scattering experiments. This system uses operational data from the APOLLO cryogenic target system to train models that predict polarization based on microwave frequency, beam current, and radiation dose. The reinforcement learning agent, trained with a reward formulation that balances performance and uncertainty, demonstrated an improvement of nearly double that of human operators. AI

IMPACT This research demonstrates a novel application of reinforcement learning for optimizing complex experimental parameters, potentially inspiring similar data-driven approaches in other scientific fields.

RANK_REASON Academic paper detailing a new methodology for a specific scientific domain. [lever_c_demoted from research: ic=1 ai=1.0]

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• kriging
• multilayer perceptron
• Nuclear Physics
• reinforcement learning

• paper
• infra

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