On-the-fly optimization of synchrotron beamlines using machine learning

T. W. Morris; M. Rakitin; A. Giles; J. Lynch; A. L. Walter; B. Nash; D. Abell; P. Moeller; I. Pogorelov; N. Goldring

doi:10.1117/12.2644996

3 October 2022 On-the-fly optimization of synchrotron beamlines using machine learning

T. W. Morris, M. Rakitin, A. Giles, J. Lynch, A. L. Walter, B. Nash, D. Abell, P. Moeller, I. Pogorelov, N. Goldring

Proceedings Volume 12222, Optical System Alignment, Tolerancing, and Verification XIV; 122220M (2022) https://doi.org/10.1117/12.2644996
Event: SPIE Optical Engineering + Applications, 2022, San Diego, California, United States

Conference Poster

Abstract

Synchrotron beamline alignment is often a cumbersome and time-intensive task due to the many degrees of freedom and the high sensitivity to misalignment of each optical element. We develop an online learning model for autonomous optimization of optical parameters using data collected from the Tender Energy X-ray Absorption Spectroscopy (TES) beamline at the National Synchrotron Light Source-II (NSLS-II). We test several optimization methods, and discuss the effectiveness of each approach, as well as their application to different optimization problems and benchmarks for beamline performance. We also discuss the practical concerns of implementing autonomous alignment systems at NSLS-II, and their potential use at other facilities.

Citation Download Citation

T. W. Morris, M. Rakitin, A. Giles, J. Lynch, A. L. Walter, B. Nash, D. Abell, P. Moeller, I. Pogorelov, and N. Goldring "On-the-fly optimization of synchrotron beamlines using machine learning", Proc. SPIE 12222, Optical System Alignment, Tolerancing, and Verification XIV, 122220M (3 October 2022); https://doi.org/10.1117/12.2644996

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