通过机器学习实现自动化光谱仪对齐
Peter Feuer-Forson1, Gregor Hartmann1, Rolf Mitzner1
1Helmholtz-Zentrum Berlin für Materialien und Energie GmbH, Albert-Einstein-Strasse 15, 12489 Berlin, Germany.
Journal of synchrotron radiation
|June 20, 2024
概括
本研究介绍了一种使用优化器和神经网络自动化光谱仪对齐的新方法. 该技术显著减少了调整时间从一个小时到五分钟,适用于各种研究仪器.
科学领域:
- 仪器仪表和测量仪器的使用
- 计算物理 计算物理
- 射线光学X射线光学
背景情况:
- 仪器对齐是实验研究中一个关键但耗时的过程.
- 尽管部件电机化,但自动调整解决方案往往缺乏.
- 移动软X射线光谱仪在实验期间需要经常进行优化.
研究的目的:
- 为移动软X射线光谱仪开发一种新的,自动化的对齐方法.
- 显著减少仪器优化所需的时间和精力.
- 为各种光束线光学元件创建适用于各种光束线光学元件的通用化方法.
主要方法:
- 使用优化器与神经网络替代模型相结合.
- 训练的神经网络完全基于模拟的光线追踪数据.
- 通过参数优化和实时验证确定模拟-实验差异.
主要成果:
- 缩短了仪器调整时间,从大约一小时减少到大约五分钟.
- 使用实验性光束线数据证明了成功的实时验证.
- 验证了神经网络代理模型方法的有效性.
结论:
- 拟议的方法可以大幅减少对齐开销.
- 这种方法可用于研究设施的其他光学元件对齐.
- 调整过程的自动化可以显著提高研究效率.
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