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.
The Sirepo-Bluesky library allows the performing of various types of Bluesky scans with Sirepo simulations acting as virtual beamlines and registration of the results with the Databroker library. We report on the progress made since the previous SPIE’2020. In particular, the support for Shadow3 and MAD-X simulation codes in Sirepo was added to the Sirepo-Bluesky library, and the API for the support of the Sirepo/SRW code was refactored. Significant efforts were put into reliable testing and documentation. A “digital twin” of the future NSLS-II ARI beamline was created and the future Bluesky scans were prototyped using the Sirepo/SRW simulations. This approach enables new optimization methods for automated instrument alignment based on the Ophyd/Bluesky and makes them transferable from simulated to various hardware backends.
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