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Jason Jaquith

at SLAC National Accelerator Lab

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Proceedings Article | 5 October 2023 Paper

Machine learning-based alignment for LCLS-II-HE optics

Aashwin Mishra, Nicholas Brennan, Tianyu Huang, Jason Jaquith, Hasan Yavas, Matthew Seaberg

Proceedings Volume 12697, 1269706 (2023) https://doi.org/10.1117/12.2677220

KEYWORDS: Machine learning, Monochromators, X-rays, Simulations, Optical alignment, Mathematical optimization, Crystals, Thermal deformation, Neural networks, Tolerancing

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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 (approximately ten 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 using available (both invasive and non-invasive) x-ray beam diagnostics. In simulations, we have demonstrated the ability of trained Machine Learning models to correct misalignments to maintain the desired central energy and optical axis within the necessary tolerances. Additionally, we exhibit the use of Bayesian Optimization to minimize the impact of thermal deformations of crystals as well as beam alignment from scratch. The initial results are very promising and efforts to further extend this work are ongoing.

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