Latent Bayesian optimization for the autonomous alignment of synchrotron beamlines

T. W. Morris; Y. Du; M. Fedurin; A. C. Giles; P. Moeller; B. Nash; M. Rakitin; B. Romasky; A. L. Walter; N. Wilson; A. Wojdyla

doi:10.1117/12.2677895

5 October 2023 Latent Bayesian optimization for the autonomous alignment of synchrotron beamlines

T. W. Morris, Y. Du, M. Fedurin, A. C. Giles, P. Moeller, B. Nash, M. Rakitin, B. Romasky, A. L. Walter, N. Wilson, A. Wojdyla

Author Affiliations +

Proceedings Volume 12697, Advances in Computational Methods for X-Ray Optics VI; 126970B (2023) https://doi.org/10.1117/12.2677895
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

Abstract

The autonomous alignment of synchrotron beamlines is typically a high-dimensional, high-overhead optimization problem, requiring us to predict a fitness function in many dimensions using relatively few data points. A model that performs well under these conditions is a Gaussian process, upon which we can apply the framework of classical Bayesian optimization methods. We show that even with no prior data, a tailored Bayesian optimization algorithm is capable of autonomously aligning up to eight dimensions of a digital twin of the TES beamline at NSLS-II in only a few minutes. We implement this approach in a software package for automatic beamline alignment, which is available out-of-the-box for any facility that leverages the Bluesky environment for beamline manipulation and data acquisition.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

T. W. Morris, Y. Du, M. Fedurin, A. C. Giles, P. Moeller, B. Nash, M. Rakitin, B. Romasky, A. L. Walter, N. Wilson, and A. Wojdyla "Latent Bayesian optimization for the autonomous alignment of synchrotron beamlines", Proc. SPIE 12697, Advances in Computational Methods for X-Ray Optics VI, 126970B (5 October 2023); https://doi.org/10.1117/12.2677895

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available