[BTeam] A General Bayesian Algorithm for the Autonomous Alignment of Beamlines

[Jay Benesch]

Almost all the work was on photon beam lines at light sources.  The 80 
MeV BNL Accelerator Test Facility was adjusted, albeit only four degrees 
of freedom.  The comments about the advantages of this approach over 
reinforcement learning may be of interest.  or not.



https://arxiv.org/abs/2402.16716

T. W. Morris, M. Rakitin, A. Islegen-Wojdyla, Y. Du, M. Fedurin, A. C. 
Giles, D. Leshchev, W. H. Li, P. Moeller, B. Nash, B. Romasky, E. 
Stavitski, A. L. Walter

Autonomous methods to align beamlines can decrease the amount of time 
spent on diagnostics, and also uncover better global optima leading to 
better beam quality. The alignment of these beamlines is a 
high-dimensional, expensive-to-sample optimization problem involving the 
simultaneous treatment of many optical elements with correlated and 
nonlinear dynamics. Bayesian optimization is a strategy of efficient 
global optimization that has proved successful in similar regimes in a 
wide variety of beamline alignment applications, though it has typically 
been implemented for particular beamlines and optimization tasks. In 
this paper, we present a basic formulation of Bayesian inference and 
Gaussian process models as they relate to multiobjective Bayesian 
optimization, as well as the practical challenges presented by beamline 
alignment. We show that the same general implementation of Bayesian 
optimization with special consideration for beamline alignment can 
quickly learn the dynamics of particular beamlines in an online fashion 
through hyperparameter fitting with no prior information. We present the 
implementation of a concise software framework for beamline alignment 
and test it on four different optimization problems for experiments at 
x-ray beamlines of the National Synchrotron Light Source II and the 
Advanced Light Source and an electron beam at the Accelerator Test 
Facility, along with benchmarking on a simulated digital twin. We 
discuss new applications of the framework, and the potential for a 
unified approach to beamline alignment at synchrotron facilities.
Subjects:	Accelerator Physics (physics.acc-ph); Instrumentation and 
Detectors (physics.ins-det); Optics (physics.optics)