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Funding: This work has benefited from the use of the LANSCE at LANL. This facility is funded by the US DOE and operated by LANS for NSSA under Contract DE-AC52-06NA25396. LA-UR-08-03585.
Tuning particle accelerators is time consuming and expensive, with a number of inherently non-linear interactions between system components. Conventional control methods have not been successful in this domain, and the result is constant and expensive monitoring of the systems by human operators. This is particularly true for the start-up and conditioning phase after a maintenance period or an unexpected fault. In turn, this often requires a step by step restart of the accelerator. Surprisingly few attempts have been made to apply intelligent accelerator control techniques to help with beam tuning, fault detection, and fault recovery problems. The reason for that might be that accelerator facilities are rare and difficult to understand systems that require detailed expert knowledge about the underlying physics as well as months if not years of experience to understand the relationship between individual components, particularly if they are geographically disjoint. This paper will give an overview about the research effort in the accelerator community that has been dedicated to the use of artificial intelligence methods for accelerator beam line tuning.
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