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Description
The slow extracted beams at the CERN Super Proton Synchrotron (SPS) are transported over several 100 m long transfer lines to three targets in the North Area Experimental Hall. The experiments require to eliminate intensity fluctuations over the roughly 5 s particle spill and hence to debunch the extracted beams. In this environment, secondary emission monitors (SEMs) have to replace the conventional beam position monitoring systems that rely on RF structure. Such monitors can be used to infer the intensity difference between two split foils, but do not readily provide position readings. Moreover, when the beam ends up on one of the foils, determining the appropriate corrector magnet settings remains a challenging task, as it is not possible to directly infer the beam deviation from the SEMs. In such scenarios, traditional trajectory control algorithms fail.
This paper summarises the application of reinforcement learning (RL) to successfully correct the beam trajectory using SEM readings. The RL policy is learnt offline in simulation, and can be successfully transferred to the real environment. Moreover, the RL policy is also tested in scenarios where the beam is lost in the line, and threading actions are needed. Results of the application of the RL policies in the real transfer line, and the different tests carried out in simulation are presented.
