Deep learning for estimation of Kirkpatrick–Baez mirror alignment errors

Jia-Nan Xie; Hui Jiang; Ai-Guo Li; Na-Xi Tian; Shuai Yan; Dong-Xu Liang; Jun Hu

doi:10.1007/s41365-023-01282-4

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Deep learning for estimation of Kirkpatrick–Baez mirror alignment errors

SYNCHROTRON RADIATION TECHNOLOGY AND APPLICATIONS | Updated：2023-09-19

- Deep learning for estimation of Kirkpatrick–Baez mirror alignment errors
- Nuclear Science and Techniques Vol. 34, Issue 8, Article number: 122(2023)
- Affiliations：
  
  1.Shanghai Synchrotron Radiation Facility, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201204, China
  2.ShanghaiTech University, Shanghai 201210, China
  3.Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
  4.University of Chinese Academy of Sciences, Beijing 100049, China
- Author bio：
  
  * Hui Jiang, jiangh@sari.ac.cn
  **Aiguo Li, liag@sari.ac.cn
- Funds：
- DOI：10.1007/s41365-023-01282-4
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Jia-Nan Xie, Hui Jiang, Ai-Guo Li, et al. Deep learning for estimation of Kirkpatrick–Baez mirror alignment errors. [J]. Nuclear Science and Techniques 34(8):122(2023)
DOI：

Jia-Nan Xie, Hui Jiang, Ai-Guo Li, et al. Deep learning for estimation of Kirkpatrick–Baez mirror alignment errors. [J]. Nuclear Science and Techniques 34(8):122(2023) DOI： 10.1007/s41365-023-01282-4.

摘要

Abstract

A deep learning-based automated Kirkpatrick–Baez mirror alignment method is proposed for synchrotron radiation. We trained a convolutional neural network (CNN) on simulated and experimental imaging data of a focusing system. Instead of learning directly from bypass images, we use a scatterer for X-ray modulation and speckle generation for image feature enhancement. The smallest normalized root mean square error on the validation set was 4%. Compared with conventional alignment methods based on motor scanning and analyzer setups, the present method simplified the optical layout and estimated alignment errors using a single-exposure experiment. Single-shot misalignment error estimation only took 0.13 s, significantly outperforming conventional methods. We also demonstrated the effects of the beam quality and pretraining using experimental data. The proposed method exhibited strong robustness, can handle high-precision focusing systems with complex or dynamic wavefront errors, and provides an important basis for intelligent control of future synchrotron radiation beamlines.

关键词

Keywords

Deep learningSynchrotron radiationOptics alignment

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