论文标题
通过机器学习进行LINAC优化的诊断
Diagnostics for Linac Optimization With Machine Learning
论文作者
论文摘要
Fermilab Linac将400 MEV H-梁传递到加速器链的其余部分。提供稳定的强度，能量和发射量是关键，因为它直接影响下游机器。为了应对以下各种效果的各种效果来抵抗LINAC输出的波动，我们正在基于机器学习（ML）实施动态纵向参数优化。作为ML模型的输入，梁诊断的信号必须充分理解和可靠。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。
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.
