Reinforcement learning algorithms have risen in popularity in the accelerator physics community in recent years, showing potential in beam control and in the optimization and automation of tasks in accelerator operation. The Helmholtz AI project “Machine Learning Toward Autonomous Accelerators” is a collaboration between DESY and KIT that works on investigating and developing reinforcement learning applications for the automatic start-up of electron linear accelerators. The work is carried out in parallel at two similar research accelerators: ARES at DESY and FLUTE at KIT, giving the unique opportunity of transfer learning between facilities. One of the first steps of this project is the establishment of a common interface between the simulations and the machine, in order to test and apply various optimization approaches interchangeably between the two accelerators. In this paper we present first results on the common interface and its application to beam focusing in ARES as well as the idea of laser shaping with spatial light modulators at FLUTE.

Annika Eichler et al., First Steps Toward an Autonomous Accelerator, a Common Project Between DESY and KIT, Proceedings of the 12th International Particle Accelerator Conference