MACHINE LEARNING METHODS FOR SINGLE SHOT RF TUNING

The European Spallation Source, currently under construction in Lund, Sweden, will be the world's most powerful neutron source. It is driven by a proton linac with a current of 62.5 mA, 2.86 ms long pulses at 14 Hz. The final section of its normal-conducting front-end consists of a 39 m long drift tube linac (DTL) divided into five tanks, designed to accelerate the proton beam from 3.6 MeV to 90 MeV. The high beam current and power impose challenges to the design and tuning of the machine and the RF amplitude and phase have to be set within 1% and 1? of the design values. The usual method used to define the RF set-point is signature matching, which can be a challenging process, and new techniques to meet the growing complexity of accelerator facilities are highly desirable. In this paper we study the use of ML to determine the RF optimum amplitude and phase, using a single pass of the beam through the ESS DTL1 tank. This novel method is compared with the more established methods using scans over RF phase, providing similar results in terms of accuracy for simulated data with errors. We also discuss the results and future extension of the method to the whole ESS DTL.