强化学习优化核物理实验

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-05 04:00

研究人员开发了一个新颖的数据驱动控制框架，该框架利用强化学习和代理模型来优化核物理散射实验中的目标极化。该系统使用APOLLO低温靶系统的运行数据来训练模型，这些模型根据微波频率、束流和辐射剂量预测极化。通过平衡性能和不确定性的奖励公式进行训练的强化学习代理，其性能比人工操作员提高了近一倍。 AI

影响这项研究展示了强化学习在优化复杂实验参数方面的新颖应用，有可能启发其他科学领域采用类似的数据驱动方法。

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arXiv cs.AI TIER_1 English(EN) · Armen Kasparian, Torri Jeske, Monibor Rahman, Chris Keith, James Maxwell, Thomas Britton, Malachi Schram, David Lawrence · 2026-10-05 04:00

用于核物理散射实验中目标极化优化的强化学习技术

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 and evolving material properties, a task that is traditionally p…