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arXiv AI By Armen Kasparian, Torri Jeske, Monibor Rahman, Chris Keith, James Maxwell, Thomas Britton, Malachi Schram, David Lawrence

Reinforcement Learning Techniques for the Optimization of Target Polarization in Nuclear Physics Scattering Experiments

Read the original on arXiv AI →

The paper introduces a data‑driven control framework that uses surrogate modeling and reinforcement learning to optimize target polarization in nuclear physics scattering experiments. By training Gaussian process and multilayer perceptron models on operational data from the APOLLO cryogenic target system, the authors demonstrate that Gaussian processes provide calibrated uncertainty estimates and better handle distributional shifts. Embedding the surrogate model in a simulation environment, a reinforcement learning agent trained with a lower‑confidence‑bound reward achieves nearly a 2× improvement over manual operator tuning.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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