基于机器学习技术的时间漂移感知射频优化
Time-drift Aware RF Optimization with Machine Learning Techniques
AI总结：
本文研究利用机器学习算法实现费米实验室直线加速器的自动化射频调谐，通过时间漂移感知建模同时优化输出能量并校正相位振荡，以应对环境变化引起的腔体频率漂移和离子源波动。
摘要：
费米实验室直线加速器向加速器链的其余部分输送400 MeV的H-束流。提供稳定的流强、能量和发射度至关重要，因为这直接影响下游机器。为了运行高电流束流，加速器必须尽量减少不受控的粒子损失；这可以通过射频参数优化来最小化束流纵向发射度来实现。然而，由于加速腔的谐振频率受环境温度和湿度变化的影响并随时间漂移，因此需要每天进行射频调谐。此外，从离子源出射的粒子的能量和相空间分布也会受到波动的影响。这种漂移并非费米实验室所独有，而是影响大多数实验室。我们正在探索机器学习（ML）算法用于自动化射频调谐，以实现两个目标：直线加速器输出能量优化和相位振荡校正，重点在于能够解释随时间变化条件的时间漂移感知建模。
英文摘要：
The Fermilab Linac delivers 400 MeV H- beam to the rest of the accelerator chain. Providing stable intensity, energy, and emittance is key since it directly affects downstream machines. To operate high current beam, accelerators must minimize uncontrolled particle loss; this can be accomplished by minimizing beam longitudinal emittance via RF parameter optimization. However, RF tuning is required daily since the resonance frequency of the accelerating cavities is affected by ambient temperature and humidity variations and thus drifts with time. In addition, the energy and phase space distribution of particles emerging from the ion source are subject to fluctuations. Such drift is not unique to Fermilab, but rather affects most laboratories. We are exploring machine learning (ML) algorithms for automated RF tuning for 2 objectives: optimization of Linac output energy and phase oscillation correction, with an emphasis on time-drift aware modeling that can account for conditions changing over time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
