Reinforcement learning techniques optimize target polarization in nuclear physics scattering experiments using data-driven control and surrogate modeling; arXiv:2610.02452v1
Read the original at arxiv.org→arXiv:2610.02452v1 Announce Type: new Abstract: The operation of dynamically polarized targets in nuclear physics experiments relies on continuous tuning of the microwave frequency to compensate for radiation damage...
Original headline: "Reinforcement Learning Techniques for the Optimization of Target Polarization in Nuclear Physics Scattering Experiments"
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- Oct 5, 04:00 UTC arXiv cs.AI lead source Reinforcement Learning Techniques for the Optimization of Target Polarization in Nuclear Physics Scattering Experiments