An algorithm for online optimization of accelerators

Journal Article · Tue Oct 01 00:00:00 EDT 2013 · Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment

DOI:https://doi.org/10.1016/j.nima.2013.05.046· OSTI ID:1074171

Huang, Xiaobiao ^[1]; Corbett, Jeff ^[1]; Safranek, James ^[1]; Wu, Juhao ^[1]

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1. SLAC National Accelerator Lab., Menlo Park, CA (United States)

We developed a general algorithm for online optimization of accelerator performance, i.e., online tuning, using the performance measure as the objective function. This method, named robust conjugate direction search (RCDS), combines the conjugate direction set approach of Powell's method with a robust line optimizer which considers the random noise in bracketing the minimum and uses parabolic fit of data points that uniformly sample the bracketed zone. Moreover, it is much more robust against noise than traditional algorithms and is therefore suitable for online application. Simulation and experimental studies have been carried out to demonstrate the strength of the new algorithm.

• Research Organization: SLAC National Accelerator Lab., Menlo Park, CA (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Basic Energy Sciences (BES)
• DOE Contract Number: AC02-76SF00515
• OSTI ID: 1074171
• Report Number(s): SLAC-PUB-15414
• Journal Information: Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 726, Issue C; ISSN 0168-9002
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

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