Abstract
Particle storage rings are a rich application domain for online optimization algorithms. The Cornell Electron Storage Ring (CESR) has hundreds of independently powered magnets, making it a high-dimensional test-problem for algorithmic tuning. We investigate algorithms that restrict the search space to a small number of linear combinations of parameters ("knobs") which contain most of the effect on our chosen objective (the vertical emittance), thus enabling efficient tuning. We report experimental tests at CESR that use dimension-reduction techniques to transform an 81-dimensional space to an 8-dimensional one which may be efficiently minimized using one-dimensional parameter scans. We also report an experimental test of a multi-objective genetic algorithm using these knobs that results in emittance improvements comparable to state-of-the-art algorithms, but with increased control over orbit errors.
Highlights
Despite the great care taken in accelerator design and fabrication, inevitable magnet misalignments, calibration errors, and drifts will result in suboptimal beam properties
By making the proper choice of decision variables, we are able to reduce the 81-dimensional task of tuning the vertical emittance at Cornell Electron Storage Ring (CESR) to an 8-dimensional problem with little loss in our ability to minimize the beam size
These few stiff knobs enable the efficient use of the robust conjugate direction search (RCDS) algorithm for tuning the machine
Summary
Despite the great care taken in accelerator design and fabrication, inevitable magnet misalignments, calibration errors, and drifts will result in suboptimal beam properties. This paper reports the results of testing the performance of candidate algorithms in both experiment and simulation on the Cornell Electron Storage Ring (CESR) These results are of interest to optimal control theorists, demonstrating real-world success with a high dimensional test case, and to the accelerator community, as a working solution to a problem of ever greater practical importance. The RCDS method makes use of simulation to obtain the Hessian matrix for the merit function with respect to corrector magnets and makes corrections to the real machine using the eigenvectors of this matrix These eigenvectors are conjugate directions, and have the property that optimizing along one direction does not require.
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