Machine Learning Based Alignment For LCLS-II-HE Optics
Aashwin Mishra, Nicholas Brennan, Tianyu Huang, Jason Jaquith, Hasan, Yavas, Matthew Seaberg

TL;DR
This paper explores machine learning techniques to correct optical misalignments and thermal deformations in the upgraded LCLS-II-HE X-ray instruments, aiming to maintain precise energy and alignment tolerances.
Contribution
It introduces the application of machine learning and Bayesian optimization for real-time correction of optical misalignments and thermal effects in high-precision X-ray instruments.
Findings
ML models effectively correct misalignments in simulations
Bayesian Optimization reduces thermal deformation impacts
Initial results show promising maintenance of optical tolerances
Abstract
The hard X-ray instruments at the Linac Coherent Light Source are in the design phase for upgrades that will take full advantage of the high repetition rates that will become available with LCLS-II-HE. The current X-ray Correlation Spectroscopy instrument will be converted to the Dynamic X-ray Scattering instrument, and will feature a meV-scale high-resolution monochromator at its front end with unprecedented coherent flux. With the new capability come many engineering and design challenges, not least of which is the sensitivity to long-term drift of the optics. With this in mind, we have estimated the system tolerance to angular drift and vibration for all the relevant optics (10 components) in terms of how the central energy out of the monochromator will be affected to inform the mechanical design. Additionally, we have started planning for methods to correct for such drifts…
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Taxonomy
TopicsAdvanced X-ray Imaging Techniques · Scientific Computing and Data Management · Machine Learning in Materials Science
