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arXiv 2209.02526physics.acc-ph
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利用机器学习诊断直线加速器优化

Diagnostics for Linac Optimization With Machine Learning

R. Sharankova, M. Mwaniki, K. Seiya, M. Wesley

摘要:

费米实验室直线加速器提供400兆电子伏的H-束流到加速器链的其余部分。提供稳定的强度、能量和发射率是关键,因为它直接影响下游机器。为了应对由于下面将要描述的各种影响而导致的直线加速器输出的波动,我们正在努力实现基于机器学习(ML)的动态纵向参数优化。作为ML模型的输入,来自光束诊断的信号必须被很好地理解和可靠。在本文中,我们讨论了基于ML的优化以及诊断研究的初步结果的状态和计划。

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 counter fluctuations of Linac output due to various effects to be described below we are working on implementing dynamic longitudinal parameter optimization based on Machine Learning (ML). As inputs for the ML model, signals from beam diagnostics have to be well understood and reliable. In this paper we discuss the status and plans for ML-based optimization as well as preliminary results of diagnostics studies.

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。

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